diff --git a/notebooks/kinfraglib/4_4_combinatorial_library_comparison_chembl.ipynb b/notebooks/kinfraglib/4_4_combinatorial_library_comparison_chembl.ipynb
index e330635b..451f5c31 100644
--- a/notebooks/kinfraglib/4_4_combinatorial_library_comparison_chembl.ipynb
+++ b/notebooks/kinfraglib/4_4_combinatorial_library_comparison_chembl.ipynb
@@ -1093,7 +1093,7 @@
      "output_type": "stream",
      "text": [
       "Number of queries: 286\n",
-      "Number of query results: 347\n"
+      "Number of query results: 345\n"
      ]
     }
    ],
@@ -1164,9 +1164,9 @@
        "      <th>0</th>\n",
        "      <td>ChEMBL bioactivity entries</td>\n",
        "      <td>135</td>\n",
-       "      <td>111</td>\n",
-       "      <td>212</td>\n",
-       "      <td>347</td>\n",
+       "      <td>110</td>\n",
+       "      <td>210</td>\n",
+       "      <td>345</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1174,7 +1174,7 @@
       ],
       "text/plain": [
        "               filtering_step molecules targets assays activities\n",
-       "0  ChEMBL bioactivity entries       135     111    212        347"
+       "0  ChEMBL bioactivity entries       135     110    210        345"
       ]
      },
      "execution_count": 34,
@@ -1204,7 +1204,7 @@
       "text/plain": [
        "standard_units\n",
        "%            1\n",
-       "nM         345\n",
+       "nM         343\n",
        "ug.mL-1      1\n",
        "dtype: int64"
       ]
@@ -1273,17 +1273,17 @@
        "      <th>0</th>\n",
        "      <td>ChEMBL bioactivity entries</td>\n",
        "      <td>135</td>\n",
-       "      <td>111</td>\n",
-       "      <td>212</td>\n",
-       "      <td>347</td>\n",
+       "      <td>110</td>\n",
+       "      <td>210</td>\n",
+       "      <td>345</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Remove non-nM activities</td>\n",
        "      <td>134</td>\n",
-       "      <td>110</td>\n",
-       "      <td>210</td>\n",
-       "      <td>345</td>\n",
+       "      <td>109</td>\n",
+       "      <td>208</td>\n",
+       "      <td>343</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1291,8 +1291,8 @@
       ],
       "text/plain": [
        "               filtering_step molecules targets assays activities\n",
-       "0  ChEMBL bioactivity entries       135     111    212        347\n",
-       "0    Remove non-nM activities       134     110    210        345"
+       "0  ChEMBL bioactivity entries       135     110    210        345\n",
+       "0    Remove non-nM activities       134     109    208        343"
       ]
      },
      "execution_count": 37,
@@ -1458,7 +1458,7 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>342</th>\n",
+       "      <th>340</th>\n",
        "      <td>29047733</td>\n",
        "      <td>CHEMBL5739509</td>\n",
        "      <td>ADP-Glo™ Kinase Assay: The ADP-Glo™ Kinase Ass...</td>\n",
@@ -1475,7 +1475,7 @@
        "      <td>388.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>343</th>\n",
+       "      <th>341</th>\n",
        "      <td>29047754</td>\n",
        "      <td>CHEMBL5739509</td>\n",
        "      <td>ADP-Glo™ Kinase Assay: The ADP-Glo™ Kinase Ass...</td>\n",
@@ -1492,7 +1492,7 @@
        "      <td>1970.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>344</th>\n",
+       "      <th>342</th>\n",
        "      <td>29047868</td>\n",
        "      <td>CHEMBL5739509</td>\n",
        "      <td>ADP-Glo™ Kinase Assay: The ADP-Glo™ Kinase Ass...</td>\n",
@@ -1509,7 +1509,7 @@
        "      <td>987.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>345</th>\n",
+       "      <th>343</th>\n",
        "      <td>29047883</td>\n",
        "      <td>CHEMBL5739509</td>\n",
        "      <td>ADP-Glo™ Kinase Assay: The ADP-Glo™ Kinase Ass...</td>\n",
@@ -1526,7 +1526,7 @@
        "      <td>30000.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>346</th>\n",
+       "      <th>344</th>\n",
        "      <td>29047901</td>\n",
        "      <td>CHEMBL5739509</td>\n",
        "      <td>ADP-Glo™ Kinase Assay: The ADP-Glo™ Kinase Ass...</td>\n",
@@ -1544,7 +1544,7 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>345 rows × 14 columns</p>\n",
+       "<p>343 rows × 14 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -1555,11 +1555,11 @@
        "3         445285    CHEMBL734079   \n",
        "4         445286    CHEMBL820306   \n",
        "..           ...             ...   \n",
-       "342     29047733   CHEMBL5739509   \n",
-       "343     29047754   CHEMBL5739509   \n",
-       "344     29047868   CHEMBL5739509   \n",
-       "345     29047883   CHEMBL5739509   \n",
-       "346     29047901   CHEMBL5739509   \n",
+       "340     29047733   CHEMBL5739509   \n",
+       "341     29047754   CHEMBL5739509   \n",
+       "342     29047868   CHEMBL5739509   \n",
+       "343     29047883   CHEMBL5739509   \n",
+       "344     29047901   CHEMBL5739509   \n",
        "\n",
        "                                     assay_description assay_type  \\\n",
        "0    In vitro inhibitory activity against H1N9 stra...          B   \n",
@@ -1568,11 +1568,11 @@
        "3    Inhibition of Mitogen-activated protein kinase...          B   \n",
        "4      Inhibition of c-Jun N-terminal kinase 2-alpha 1          B   \n",
        "..                                                 ...        ...   \n",
+       "340  ADP-Glo™ Kinase Assay: The ADP-Glo™ Kinase Ass...          B   \n",
+       "341  ADP-Glo™ Kinase Assay: The ADP-Glo™ Kinase Ass...          B   \n",
        "342  ADP-Glo™ Kinase Assay: The ADP-Glo™ Kinase Ass...          B   \n",
        "343  ADP-Glo™ Kinase Assay: The ADP-Glo™ Kinase Ass...          B   \n",
        "344  ADP-Glo™ Kinase Assay: The ADP-Glo™ Kinase Ass...          B   \n",
-       "345  ADP-Glo™ Kinase Assay: The ADP-Glo™ Kinase Ass...          B   \n",
-       "346  ADP-Glo™ Kinase Assay: The ADP-Glo™ Kinase Ass...          B   \n",
        "\n",
        "    molecule_chembl_id relation standard_units  standard_value  \\\n",
        "0          CHEMBL71186        =             nM       1500000.0   \n",
@@ -1581,11 +1581,11 @@
        "3          CHEMBL67658        =             nM            86.0   \n",
        "4          CHEMBL67658        =             nM           910.0   \n",
        "..                 ...      ...            ...             ...   \n",
-       "342      CHEMBL6033856        =             nM           388.0   \n",
-       "343      CHEMBL5752959        =             nM          1970.0   \n",
-       "344      CHEMBL5825290        =             nM           987.0   \n",
-       "345      CHEMBL5756667        =             nM         30000.0   \n",
-       "346      CHEMBL5788689        =             nM          4894.0   \n",
+       "340      CHEMBL6033856        =             nM           388.0   \n",
+       "341      CHEMBL5752959        =             nM          1970.0   \n",
+       "342      CHEMBL5825290        =             nM           987.0   \n",
+       "343      CHEMBL5756667        =             nM         30000.0   \n",
+       "344      CHEMBL5788689        =             nM          4894.0   \n",
        "\n",
        "    target_chembl_id target_organism  \\\n",
        "0         CHEMBL3046    Homo sapiens   \n",
@@ -1594,11 +1594,11 @@
        "3      CHEMBL2094115    Homo sapiens   \n",
        "4         CHEMBL4179    Homo sapiens   \n",
        "..               ...             ...   \n",
+       "340       CHEMBL1841    Homo sapiens   \n",
+       "341       CHEMBL1841    Homo sapiens   \n",
        "342       CHEMBL1841    Homo sapiens   \n",
        "343       CHEMBL1841    Homo sapiens   \n",
        "344       CHEMBL1841    Homo sapiens   \n",
-       "345       CHEMBL1841    Homo sapiens   \n",
-       "346       CHEMBL1841    Homo sapiens   \n",
        "\n",
        "                                      target_pref_name  type units    value  \n",
        "0                                          Sialidase-3  IC50    mM      1.5  \n",
@@ -1607,13 +1607,13 @@
        "3                                       MAP kinase p38  IC50    nM     86.0  \n",
        "4                   Mitogen-activated protein kinase 9  IC50    nM    910.0  \n",
        "..                                                 ...   ...   ...      ...  \n",
-       "342                        Tyrosine-protein kinase Fyn  IC50    nM    388.0  \n",
-       "343                        Tyrosine-protein kinase Fyn  IC50    nM   1970.0  \n",
-       "344                        Tyrosine-protein kinase Fyn  IC50    nM    987.0  \n",
-       "345                        Tyrosine-protein kinase Fyn  IC50    nM  30000.0  \n",
-       "346                        Tyrosine-protein kinase Fyn  IC50    nM   4894.0  \n",
+       "340                        Tyrosine-protein kinase Fyn  IC50    nM    388.0  \n",
+       "341                        Tyrosine-protein kinase Fyn  IC50    nM   1970.0  \n",
+       "342                        Tyrosine-protein kinase Fyn  IC50    nM    987.0  \n",
+       "343                        Tyrosine-protein kinase Fyn  IC50    nM  30000.0  \n",
+       "344                        Tyrosine-protein kinase Fyn  IC50    nM   4894.0  \n",
        "\n",
-       "[345 rows x 14 columns]"
+       "[343 rows x 14 columns]"
       ]
      },
      "execution_count": 38,
@@ -1640,9 +1640,9 @@
        "Bos taurus                      2\n",
        "Ovis aries                      2\n",
        "Photinus pyralis                2\n",
-       "Photuris pennsylvanica          2\n",
+       "Photuris pensylvanica           2\n",
        "Mus musculus                    6\n",
-       "Homo sapiens                  299\n",
+       "Homo sapiens                  297\n",
        "dtype: int64"
       ]
      },
@@ -1704,25 +1704,25 @@
        "      <th>0</th>\n",
        "      <td>ChEMBL bioactivity entries</td>\n",
        "      <td>135</td>\n",
-       "      <td>111</td>\n",
-       "      <td>212</td>\n",
-       "      <td>347</td>\n",
+       "      <td>110</td>\n",
+       "      <td>210</td>\n",
+       "      <td>345</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Remove non-nM activities</td>\n",
        "      <td>134</td>\n",
-       "      <td>110</td>\n",
-       "      <td>210</td>\n",
-       "      <td>345</td>\n",
+       "      <td>109</td>\n",
+       "      <td>208</td>\n",
+       "      <td>343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Only human entries</td>\n",
        "      <td>122</td>\n",
-       "      <td>97</td>\n",
-       "      <td>178</td>\n",
-       "      <td>299</td>\n",
+       "      <td>96</td>\n",
+       "      <td>176</td>\n",
+       "      <td>297</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1730,9 +1730,9 @@
       ],
       "text/plain": [
        "               filtering_step molecules targets assays activities\n",
-       "0  ChEMBL bioactivity entries       135     111    212        347\n",
-       "0    Remove non-nM activities       134     110    210        345\n",
-       "0          Only human entries       122      97    178        299"
+       "0  ChEMBL bioactivity entries       135     110    210        345\n",
+       "0    Remove non-nM activities       134     109    208        343\n",
+       "0          Only human entries       122      96    176        297"
       ]
      },
      "execution_count": 41,
@@ -1767,9 +1767,9 @@
     {
      "data": {
       "text/plain": [
-       "1     54\n",
+       "1     55\n",
        "2     32\n",
-       "3     13\n",
+       "3     12\n",
        "4     11\n",
        "5      3\n",
        "6      3\n",
@@ -1802,7 +1802,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1839,7 +1839,7 @@
      "data": {
       "text/plain": [
        "1     163\n",
-       "2      39\n",
+       "2      38\n",
        "3       6\n",
        "4       4\n",
        "6       1\n",
@@ -1869,7 +1869,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2169,293 +2169,286 @@
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>108</td>\n",
-       "      <td>CHEMBL361153</td>\n",
-       "      <td>CHEMBL5282</td>\n",
-       "      <td>[[1370.0], [1370.0]]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>109</td>\n",
        "      <td>CHEMBL362507</td>\n",
        "      <td>CHEMBL242</td>\n",
        "      <td>[[201.0], [201.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>110</td>\n",
+       "      <th>11</th>\n",
+       "      <td>109</td>\n",
        "      <td>CHEMBL3680991</td>\n",
        "      <td>CHEMBL5247</td>\n",
        "      <td>[[1600.0], [1600.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>111</td>\n",
+       "      <th>12</th>\n",
+       "      <td>110</td>\n",
        "      <td>CHEMBL3685425</td>\n",
        "      <td>CHEMBL5251</td>\n",
        "      <td>[[33.0], [47.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>113</td>\n",
+       "      <th>13</th>\n",
+       "      <td>112</td>\n",
        "      <td>CHEMBL3700458</td>\n",
        "      <td>CHEMBL3629</td>\n",
        "      <td>[[11.0], [11.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>114</td>\n",
+       "      <th>14</th>\n",
+       "      <td>113</td>\n",
        "      <td>CHEMBL3732078</td>\n",
        "      <td>CHEMBL1841</td>\n",
        "      <td>[[1314.0], [1314.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>115</td>\n",
+       "      <th>15</th>\n",
+       "      <td>114</td>\n",
        "      <td>CHEMBL3732078</td>\n",
        "      <td>CHEMBL5678</td>\n",
        "      <td>[[80000.0], [20000.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>116</td>\n",
+       "      <th>16</th>\n",
+       "      <td>115</td>\n",
        "      <td>CHEMBL3754304</td>\n",
        "      <td>CHEMBL6166</td>\n",
        "      <td>[[64.0], [4200.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>118</td>\n",
+       "      <th>17</th>\n",
+       "      <td>117</td>\n",
        "      <td>CHEMBL3823597</td>\n",
        "      <td>CHEMBL4578</td>\n",
        "      <td>[[80.0], [5000.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>120</td>\n",
+       "      <th>18</th>\n",
+       "      <td>119</td>\n",
        "      <td>CHEMBL3921258</td>\n",
        "      <td>CHEMBL1075104</td>\n",
        "      <td>[[280.0], [340.0], [280.0], [340.0], [280.0], ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>121</td>\n",
+       "      <th>19</th>\n",
+       "      <td>120</td>\n",
        "      <td>CHEMBL3929490</td>\n",
        "      <td>CHEMBL4718</td>\n",
        "      <td>[[170.0], [64.0], [170.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>122</td>\n",
+       "      <th>20</th>\n",
+       "      <td>121</td>\n",
        "      <td>CHEMBL3938306</td>\n",
        "      <td>CHEMBL1075104</td>\n",
        "      <td>[[430.0], [370.0], [430.0], [370.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>123</td>\n",
+       "      <th>21</th>\n",
+       "      <td>122</td>\n",
        "      <td>CHEMBL3960498</td>\n",
        "      <td>CHEMBL4718</td>\n",
        "      <td>[[230.0], [118.0], [230.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>125</td>\n",
+       "      <th>22</th>\n",
+       "      <td>124</td>\n",
        "      <td>CHEMBL401695</td>\n",
        "      <td>CHEMBL1906</td>\n",
        "      <td>[[90.0], [90.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>128</td>\n",
+       "      <th>23</th>\n",
+       "      <td>127</td>\n",
        "      <td>CHEMBL4079515</td>\n",
        "      <td>CHEMBL4040</td>\n",
        "      <td>[[1.4], [7.9], [570.0], [340.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>129</td>\n",
+       "      <th>24</th>\n",
+       "      <td>128</td>\n",
        "      <td>CHEMBL4080944</td>\n",
        "      <td>CHEMBL4040</td>\n",
        "      <td>[[1.2], [9.2], [960.0], [940.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>130</td>\n",
+       "      <th>25</th>\n",
+       "      <td>129</td>\n",
        "      <td>CHEMBL4085582</td>\n",
        "      <td>CHEMBL2148</td>\n",
        "      <td>[[2280.0], [0.3], [29.0], [32.0], [0.77, 0.77]...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>131</td>\n",
+       "      <th>26</th>\n",
+       "      <td>130</td>\n",
        "      <td>CHEMBL4085582</td>\n",
        "      <td>CHEMBL2835</td>\n",
        "      <td>[[966.0], [8710.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>135</td>\n",
+       "      <th>27</th>\n",
+       "      <td>134</td>\n",
        "      <td>CHEMBL4105185</td>\n",
        "      <td>CHEMBL4040</td>\n",
        "      <td>[[36.0], [370.0], [160.0], [570.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>137</td>\n",
+       "      <th>28</th>\n",
+       "      <td>136</td>\n",
        "      <td>CHEMBL4114404</td>\n",
        "      <td>CHEMBL4040</td>\n",
        "      <td>[[3.1], [5.9]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>145</td>\n",
+       "      <th>29</th>\n",
+       "      <td>144</td>\n",
        "      <td>CHEMBL4527170</td>\n",
        "      <td>CHEMBL4204</td>\n",
        "      <td>[[12000.0], [12000.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>146</td>\n",
+       "      <th>30</th>\n",
+       "      <td>145</td>\n",
        "      <td>CHEMBL4527170</td>\n",
        "      <td>CHEMBL4718</td>\n",
        "      <td>[[35000.0], [12000.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>155</td>\n",
+       "      <th>31</th>\n",
+       "      <td>154</td>\n",
        "      <td>CHEMBL4785602</td>\n",
        "      <td>CHEMBL5432</td>\n",
        "      <td>[[1349.0], [645.0], [35.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>156</td>\n",
+       "      <th>32</th>\n",
+       "      <td>155</td>\n",
        "      <td>CHEMBL4788138</td>\n",
        "      <td>CHEMBL6166</td>\n",
        "      <td>[[12.0], [1692.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>173</td>\n",
+       "      <th>33</th>\n",
+       "      <td>172</td>\n",
        "      <td>CHEMBL502351</td>\n",
        "      <td>CHEMBL5903</td>\n",
        "      <td>[[32.0], [32.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>174</td>\n",
+       "      <th>34</th>\n",
+       "      <td>173</td>\n",
        "      <td>CHEMBL512616</td>\n",
        "      <td>CHEMBL3717</td>\n",
        "      <td>[[1900.0], [1900.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>185</td>\n",
+       "      <th>35</th>\n",
+       "      <td>184</td>\n",
        "      <td>CHEMBL5739954</td>\n",
        "      <td>CHEMBL1841</td>\n",
        "      <td>[[586.0], [586.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>188</td>\n",
+       "      <th>36</th>\n",
+       "      <td>187</td>\n",
        "      <td>CHEMBL5752959</td>\n",
        "      <td>CHEMBL1841</td>\n",
        "      <td>[[1970.0], [1970.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>189</td>\n",
+       "      <th>37</th>\n",
+       "      <td>188</td>\n",
        "      <td>CHEMBL5756667</td>\n",
        "      <td>CHEMBL1841</td>\n",
        "      <td>[[30000.0], [30000.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>190</td>\n",
+       "      <th>38</th>\n",
+       "      <td>189</td>\n",
        "      <td>CHEMBL5788689</td>\n",
        "      <td>CHEMBL1841</td>\n",
        "      <td>[[4894.0], [4894.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>191</td>\n",
+       "      <th>39</th>\n",
+       "      <td>190</td>\n",
        "      <td>CHEMBL5813268</td>\n",
        "      <td>CHEMBL1841</td>\n",
        "      <td>[[229.0], [229.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>192</td>\n",
+       "      <th>40</th>\n",
+       "      <td>191</td>\n",
        "      <td>CHEMBL5814309</td>\n",
        "      <td>CHEMBL1841</td>\n",
        "      <td>[[1015.0], [1015.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>193</td>\n",
+       "      <th>41</th>\n",
+       "      <td>192</td>\n",
        "      <td>CHEMBL5822505</td>\n",
        "      <td>CHEMBL1841</td>\n",
        "      <td>[[4642.0], [4642.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>194</td>\n",
+       "      <th>42</th>\n",
+       "      <td>193</td>\n",
        "      <td>CHEMBL5825290</td>\n",
        "      <td>CHEMBL1841</td>\n",
        "      <td>[[987.0], [987.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>195</td>\n",
+       "      <th>43</th>\n",
+       "      <td>194</td>\n",
        "      <td>CHEMBL5916482</td>\n",
        "      <td>CHEMBL1841</td>\n",
        "      <td>[[894.0], [894.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>196</td>\n",
+       "      <th>44</th>\n",
+       "      <td>195</td>\n",
        "      <td>CHEMBL5936134</td>\n",
        "      <td>CHEMBL1841</td>\n",
        "      <td>[[2005.0], [2005.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>198</td>\n",
+       "      <th>45</th>\n",
+       "      <td>197</td>\n",
        "      <td>CHEMBL5960468</td>\n",
        "      <td>CHEMBL1841</td>\n",
        "      <td>[[223.0], [223.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>202</td>\n",
+       "      <th>46</th>\n",
+       "      <td>201</td>\n",
        "      <td>CHEMBL6033856</td>\n",
        "      <td>CHEMBL1841</td>\n",
        "      <td>[[388.0], [388.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>203</td>\n",
+       "      <th>47</th>\n",
+       "      <td>202</td>\n",
        "      <td>CHEMBL6042789</td>\n",
        "      <td>CHEMBL1841</td>\n",
        "      <td>[[3843.0], [3843.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>204</td>\n",
+       "      <th>48</th>\n",
+       "      <td>203</td>\n",
        "      <td>CHEMBL64</td>\n",
        "      <td>CHEMBL2439</td>\n",
        "      <td>[[4700.0], [5000.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>205</td>\n",
+       "      <th>49</th>\n",
+       "      <td>204</td>\n",
        "      <td>CHEMBL67658</td>\n",
        "      <td>CHEMBL1906</td>\n",
        "      <td>[[2600.0], [2600.0]]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>209</td>\n",
+       "      <th>50</th>\n",
+       "      <td>208</td>\n",
        "      <td>CHEMBL72461</td>\n",
        "      <td>CHEMBL301</td>\n",
        "      <td>[[36.0], [210.0]]</td>\n",
@@ -2476,48 +2469,47 @@
        "7      92      CHEMBL3341791       CHEMBL4439   \n",
        "8     100      CHEMBL3403541       CHEMBL2971   \n",
        "9     105      CHEMBL3409588       CHEMBL4040   \n",
-       "10    108       CHEMBL361153       CHEMBL5282   \n",
-       "11    109       CHEMBL362507        CHEMBL242   \n",
-       "12    110      CHEMBL3680991       CHEMBL5247   \n",
-       "13    111      CHEMBL3685425       CHEMBL5251   \n",
-       "14    113      CHEMBL3700458       CHEMBL3629   \n",
-       "15    114      CHEMBL3732078       CHEMBL1841   \n",
-       "16    115      CHEMBL3732078       CHEMBL5678   \n",
-       "17    116      CHEMBL3754304       CHEMBL6166   \n",
-       "18    118      CHEMBL3823597       CHEMBL4578   \n",
-       "19    120      CHEMBL3921258    CHEMBL1075104   \n",
-       "20    121      CHEMBL3929490       CHEMBL4718   \n",
-       "21    122      CHEMBL3938306    CHEMBL1075104   \n",
-       "22    123      CHEMBL3960498       CHEMBL4718   \n",
-       "23    125       CHEMBL401695       CHEMBL1906   \n",
-       "24    128      CHEMBL4079515       CHEMBL4040   \n",
-       "25    129      CHEMBL4080944       CHEMBL4040   \n",
-       "26    130      CHEMBL4085582       CHEMBL2148   \n",
-       "27    131      CHEMBL4085582       CHEMBL2835   \n",
-       "28    135      CHEMBL4105185       CHEMBL4040   \n",
-       "29    137      CHEMBL4114404       CHEMBL4040   \n",
-       "30    145      CHEMBL4527170       CHEMBL4204   \n",
-       "31    146      CHEMBL4527170       CHEMBL4718   \n",
-       "32    155      CHEMBL4785602       CHEMBL5432   \n",
-       "33    156      CHEMBL4788138       CHEMBL6166   \n",
-       "34    173       CHEMBL502351       CHEMBL5903   \n",
-       "35    174       CHEMBL512616       CHEMBL3717   \n",
-       "36    185      CHEMBL5739954       CHEMBL1841   \n",
-       "37    188      CHEMBL5752959       CHEMBL1841   \n",
-       "38    189      CHEMBL5756667       CHEMBL1841   \n",
-       "39    190      CHEMBL5788689       CHEMBL1841   \n",
-       "40    191      CHEMBL5813268       CHEMBL1841   \n",
-       "41    192      CHEMBL5814309       CHEMBL1841   \n",
-       "42    193      CHEMBL5822505       CHEMBL1841   \n",
-       "43    194      CHEMBL5825290       CHEMBL1841   \n",
-       "44    195      CHEMBL5916482       CHEMBL1841   \n",
-       "45    196      CHEMBL5936134       CHEMBL1841   \n",
-       "46    198      CHEMBL5960468       CHEMBL1841   \n",
-       "47    202      CHEMBL6033856       CHEMBL1841   \n",
-       "48    203      CHEMBL6042789       CHEMBL1841   \n",
-       "49    204           CHEMBL64       CHEMBL2439   \n",
-       "50    205        CHEMBL67658       CHEMBL1906   \n",
-       "51    209        CHEMBL72461        CHEMBL301   \n",
+       "10    108       CHEMBL362507        CHEMBL242   \n",
+       "11    109      CHEMBL3680991       CHEMBL5247   \n",
+       "12    110      CHEMBL3685425       CHEMBL5251   \n",
+       "13    112      CHEMBL3700458       CHEMBL3629   \n",
+       "14    113      CHEMBL3732078       CHEMBL1841   \n",
+       "15    114      CHEMBL3732078       CHEMBL5678   \n",
+       "16    115      CHEMBL3754304       CHEMBL6166   \n",
+       "17    117      CHEMBL3823597       CHEMBL4578   \n",
+       "18    119      CHEMBL3921258    CHEMBL1075104   \n",
+       "19    120      CHEMBL3929490       CHEMBL4718   \n",
+       "20    121      CHEMBL3938306    CHEMBL1075104   \n",
+       "21    122      CHEMBL3960498       CHEMBL4718   \n",
+       "22    124       CHEMBL401695       CHEMBL1906   \n",
+       "23    127      CHEMBL4079515       CHEMBL4040   \n",
+       "24    128      CHEMBL4080944       CHEMBL4040   \n",
+       "25    129      CHEMBL4085582       CHEMBL2148   \n",
+       "26    130      CHEMBL4085582       CHEMBL2835   \n",
+       "27    134      CHEMBL4105185       CHEMBL4040   \n",
+       "28    136      CHEMBL4114404       CHEMBL4040   \n",
+       "29    144      CHEMBL4527170       CHEMBL4204   \n",
+       "30    145      CHEMBL4527170       CHEMBL4718   \n",
+       "31    154      CHEMBL4785602       CHEMBL5432   \n",
+       "32    155      CHEMBL4788138       CHEMBL6166   \n",
+       "33    172       CHEMBL502351       CHEMBL5903   \n",
+       "34    173       CHEMBL512616       CHEMBL3717   \n",
+       "35    184      CHEMBL5739954       CHEMBL1841   \n",
+       "36    187      CHEMBL5752959       CHEMBL1841   \n",
+       "37    188      CHEMBL5756667       CHEMBL1841   \n",
+       "38    189      CHEMBL5788689       CHEMBL1841   \n",
+       "39    190      CHEMBL5813268       CHEMBL1841   \n",
+       "40    191      CHEMBL5814309       CHEMBL1841   \n",
+       "41    192      CHEMBL5822505       CHEMBL1841   \n",
+       "42    193      CHEMBL5825290       CHEMBL1841   \n",
+       "43    194      CHEMBL5916482       CHEMBL1841   \n",
+       "44    195      CHEMBL5936134       CHEMBL1841   \n",
+       "45    197      CHEMBL5960468       CHEMBL1841   \n",
+       "46    201      CHEMBL6033856       CHEMBL1841   \n",
+       "47    202      CHEMBL6042789       CHEMBL1841   \n",
+       "48    203           CHEMBL64       CHEMBL2439   \n",
+       "49    204        CHEMBL67658       CHEMBL1906   \n",
+       "50    208        CHEMBL72461        CHEMBL301   \n",
        "\n",
        "                                       standard_value  \n",
        "0                            [[4.0], [4300.0], [4.0]]  \n",
@@ -2530,48 +2522,47 @@
        "7                               [[21000.0], [4300.0]]  \n",
        "8                                      [[1.0], [1.0]]  \n",
        "9                               [[3.9], [3.9], [1.1]]  \n",
-       "10                               [[1370.0], [1370.0]]  \n",
-       "11                                 [[201.0], [201.0]]  \n",
-       "12                               [[1600.0], [1600.0]]  \n",
-       "13                                   [[33.0], [47.0]]  \n",
-       "14                                   [[11.0], [11.0]]  \n",
-       "15                               [[1314.0], [1314.0]]  \n",
-       "16                             [[80000.0], [20000.0]]  \n",
-       "17                                 [[64.0], [4200.0]]  \n",
-       "18                                 [[80.0], [5000.0]]  \n",
-       "19  [[280.0], [340.0], [280.0], [340.0], [280.0], ...  \n",
-       "20                         [[170.0], [64.0], [170.0]]  \n",
-       "21               [[430.0], [370.0], [430.0], [370.0]]  \n",
-       "22                        [[230.0], [118.0], [230.0]]  \n",
-       "23                                   [[90.0], [90.0]]  \n",
-       "24                   [[1.4], [7.9], [570.0], [340.0]]  \n",
-       "25                   [[1.2], [9.2], [960.0], [940.0]]  \n",
-       "26  [[2280.0], [0.3], [29.0], [32.0], [0.77, 0.77]...  \n",
-       "27                                [[966.0], [8710.0]]  \n",
-       "28                [[36.0], [370.0], [160.0], [570.0]]  \n",
-       "29                                     [[3.1], [5.9]]  \n",
-       "30                             [[12000.0], [12000.0]]  \n",
-       "31                             [[35000.0], [12000.0]]  \n",
-       "32                        [[1349.0], [645.0], [35.0]]  \n",
-       "33                                 [[12.0], [1692.0]]  \n",
-       "34                                   [[32.0], [32.0]]  \n",
-       "35                               [[1900.0], [1900.0]]  \n",
-       "36                                 [[586.0], [586.0]]  \n",
-       "37                               [[1970.0], [1970.0]]  \n",
-       "38                             [[30000.0], [30000.0]]  \n",
-       "39                               [[4894.0], [4894.0]]  \n",
-       "40                                 [[229.0], [229.0]]  \n",
-       "41                               [[1015.0], [1015.0]]  \n",
-       "42                               [[4642.0], [4642.0]]  \n",
-       "43                                 [[987.0], [987.0]]  \n",
-       "44                                 [[894.0], [894.0]]  \n",
-       "45                               [[2005.0], [2005.0]]  \n",
-       "46                                 [[223.0], [223.0]]  \n",
-       "47                                 [[388.0], [388.0]]  \n",
-       "48                               [[3843.0], [3843.0]]  \n",
-       "49                               [[4700.0], [5000.0]]  \n",
-       "50                               [[2600.0], [2600.0]]  \n",
-       "51                                  [[36.0], [210.0]]  "
+       "10                                 [[201.0], [201.0]]  \n",
+       "11                               [[1600.0], [1600.0]]  \n",
+       "12                                   [[33.0], [47.0]]  \n",
+       "13                                   [[11.0], [11.0]]  \n",
+       "14                               [[1314.0], [1314.0]]  \n",
+       "15                             [[80000.0], [20000.0]]  \n",
+       "16                                 [[64.0], [4200.0]]  \n",
+       "17                                 [[80.0], [5000.0]]  \n",
+       "18  [[280.0], [340.0], [280.0], [340.0], [280.0], ...  \n",
+       "19                         [[170.0], [64.0], [170.0]]  \n",
+       "20               [[430.0], [370.0], [430.0], [370.0]]  \n",
+       "21                        [[230.0], [118.0], [230.0]]  \n",
+       "22                                   [[90.0], [90.0]]  \n",
+       "23                   [[1.4], [7.9], [570.0], [340.0]]  \n",
+       "24                   [[1.2], [9.2], [960.0], [940.0]]  \n",
+       "25  [[2280.0], [0.3], [29.0], [32.0], [0.77, 0.77]...  \n",
+       "26                                [[966.0], [8710.0]]  \n",
+       "27                [[36.0], [370.0], [160.0], [570.0]]  \n",
+       "28                                     [[3.1], [5.9]]  \n",
+       "29                             [[12000.0], [12000.0]]  \n",
+       "30                             [[35000.0], [12000.0]]  \n",
+       "31                        [[1349.0], [645.0], [35.0]]  \n",
+       "32                                 [[12.0], [1692.0]]  \n",
+       "33                                   [[32.0], [32.0]]  \n",
+       "34                               [[1900.0], [1900.0]]  \n",
+       "35                                 [[586.0], [586.0]]  \n",
+       "36                               [[1970.0], [1970.0]]  \n",
+       "37                             [[30000.0], [30000.0]]  \n",
+       "38                               [[4894.0], [4894.0]]  \n",
+       "39                                 [[229.0], [229.0]]  \n",
+       "40                               [[1015.0], [1015.0]]  \n",
+       "41                               [[4642.0], [4642.0]]  \n",
+       "42                                 [[987.0], [987.0]]  \n",
+       "43                                 [[894.0], [894.0]]  \n",
+       "44                               [[2005.0], [2005.0]]  \n",
+       "45                                 [[223.0], [223.0]]  \n",
+       "46                                 [[388.0], [388.0]]  \n",
+       "47                               [[3843.0], [3843.0]]  \n",
+       "48                               [[4700.0], [5000.0]]  \n",
+       "49                               [[2600.0], [2600.0]]  \n",
+       "50                                  [[36.0], [210.0]]  "
       ]
      },
      "execution_count": 49,
@@ -2655,33 +2646,33 @@
        "      <th>0</th>\n",
        "      <td>ChEMBL bioactivity entries</td>\n",
        "      <td>135</td>\n",
-       "      <td>111</td>\n",
-       "      <td>212</td>\n",
-       "      <td>347</td>\n",
+       "      <td>110</td>\n",
+       "      <td>210</td>\n",
+       "      <td>345</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Remove non-nM activities</td>\n",
        "      <td>134</td>\n",
-       "      <td>110</td>\n",
-       "      <td>210</td>\n",
-       "      <td>345</td>\n",
+       "      <td>109</td>\n",
+       "      <td>208</td>\n",
+       "      <td>343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Only human entries</td>\n",
        "      <td>122</td>\n",
-       "      <td>97</td>\n",
-       "      <td>178</td>\n",
-       "      <td>299</td>\n",
+       "      <td>96</td>\n",
+       "      <td>176</td>\n",
+       "      <td>297</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Get minimum IC50 per molecule-target pair</td>\n",
        "      <td>122</td>\n",
-       "      <td>97</td>\n",
-       "      <td>130</td>\n",
-       "      <td>215</td>\n",
+       "      <td>96</td>\n",
+       "      <td>129</td>\n",
+       "      <td>214</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2689,16 +2680,16 @@
       ],
       "text/plain": [
        "                              filtering_step molecules targets assays  \\\n",
-       "0                 ChEMBL bioactivity entries       135     111    212   \n",
-       "0                   Remove non-nM activities       134     110    210   \n",
-       "0                         Only human entries       122      97    178   \n",
-       "0  Get minimum IC50 per molecule-target pair       122      97    130   \n",
+       "0                 ChEMBL bioactivity entries       135     110    210   \n",
+       "0                   Remove non-nM activities       134     109    208   \n",
+       "0                         Only human entries       122      96    176   \n",
+       "0  Get minimum IC50 per molecule-target pair       122      96    129   \n",
        "\n",
        "  activities  \n",
-       "0        347  \n",
        "0        345  \n",
-       "0        299  \n",
-       "0        215  "
+       "0        343  \n",
+       "0        297  \n",
+       "0        214  "
       ]
      },
      "execution_count": 51,
@@ -2922,33 +2913,33 @@
        "      <th>0</th>\n",
        "      <td>ChEMBL bioactivity entries</td>\n",
        "      <td>135</td>\n",
-       "      <td>111</td>\n",
-       "      <td>212</td>\n",
-       "      <td>347</td>\n",
+       "      <td>110</td>\n",
+       "      <td>210</td>\n",
+       "      <td>345</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Remove non-nM activities</td>\n",
        "      <td>134</td>\n",
-       "      <td>110</td>\n",
-       "      <td>210</td>\n",
-       "      <td>345</td>\n",
+       "      <td>109</td>\n",
+       "      <td>208</td>\n",
+       "      <td>343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Only human entries</td>\n",
        "      <td>122</td>\n",
-       "      <td>97</td>\n",
-       "      <td>178</td>\n",
-       "      <td>299</td>\n",
+       "      <td>96</td>\n",
+       "      <td>176</td>\n",
+       "      <td>297</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Get minimum IC50 per molecule-target pair</td>\n",
        "      <td>122</td>\n",
-       "      <td>97</td>\n",
-       "      <td>130</td>\n",
-       "      <td>215</td>\n",
+       "      <td>96</td>\n",
+       "      <td>129</td>\n",
+       "      <td>214</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
@@ -2964,17 +2955,17 @@
       ],
       "text/plain": [
        "                              filtering_step molecules targets assays  \\\n",
-       "0                 ChEMBL bioactivity entries       135     111    212   \n",
-       "0                   Remove non-nM activities       134     110    210   \n",
-       "0                         Only human entries       122      97    178   \n",
-       "0  Get minimum IC50 per molecule-target pair       122      97    130   \n",
+       "0                 ChEMBL bioactivity entries       135     110    210   \n",
+       "0                   Remove non-nM activities       134     109    208   \n",
+       "0                         Only human entries       122      96    176   \n",
+       "0  Get minimum IC50 per molecule-target pair       122      96    129   \n",
        "0        Only \"active\" molecule-target pairs        82      61     82   \n",
        "\n",
        "  activities  \n",
-       "0        347  \n",
        "0        345  \n",
-       "0        299  \n",
-       "0        215  \n",
+       "0        343  \n",
+       "0        297  \n",
+       "0        214  \n",
        "0        138  "
       ]
      },
@@ -5463,33 +5454,33 @@
        "      <th>0</th>\n",
        "      <td>ChEMBL bioactivity entries</td>\n",
        "      <td>135</td>\n",
-       "      <td>111</td>\n",
-       "      <td>212</td>\n",
-       "      <td>347</td>\n",
+       "      <td>110</td>\n",
+       "      <td>210</td>\n",
+       "      <td>345</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Remove non-nM activities</td>\n",
        "      <td>134</td>\n",
-       "      <td>110</td>\n",
-       "      <td>210</td>\n",
-       "      <td>345</td>\n",
+       "      <td>109</td>\n",
+       "      <td>208</td>\n",
+       "      <td>343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Only human entries</td>\n",
        "      <td>122</td>\n",
-       "      <td>97</td>\n",
-       "      <td>178</td>\n",
-       "      <td>299</td>\n",
+       "      <td>96</td>\n",
+       "      <td>176</td>\n",
+       "      <td>297</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Get minimum IC50 per molecule-target pair</td>\n",
        "      <td>122</td>\n",
-       "      <td>97</td>\n",
-       "      <td>130</td>\n",
-       "      <td>215</td>\n",
+       "      <td>96</td>\n",
+       "      <td>129</td>\n",
+       "      <td>214</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
@@ -5513,18 +5504,18 @@
       ],
       "text/plain": [
        "                              filtering_step molecules targets assays  \\\n",
-       "0                 ChEMBL bioactivity entries       135     111    212   \n",
-       "0                   Remove non-nM activities       134     110    210   \n",
-       "0                         Only human entries       122      97    178   \n",
-       "0  Get minimum IC50 per molecule-target pair       122      97    130   \n",
+       "0                 ChEMBL bioactivity entries       135     110    210   \n",
+       "0                   Remove non-nM activities       134     109    208   \n",
+       "0                         Only human entries       122      96    176   \n",
+       "0  Get minimum IC50 per molecule-target pair       122      96    129   \n",
        "0        Only \"active\" molecule-target pairs        82      61     82   \n",
        "0                 Only molecule-kinase pairs        80      59     80   \n",
        "\n",
        "  activities  \n",
-       "0        347  \n",
        "0        345  \n",
-       "0        299  \n",
-       "0        215  \n",
+       "0        343  \n",
+       "0        297  \n",
+       "0        214  \n",
        "0        138  \n",
        "0        136  "
       ]
@@ -8033,33 +8024,33 @@
        "      <th>0</th>\n",
        "      <td>ChEMBL bioactivity entries</td>\n",
        "      <td>135</td>\n",
-       "      <td>111</td>\n",
-       "      <td>212</td>\n",
-       "      <td>347</td>\n",
+       "      <td>110</td>\n",
+       "      <td>210</td>\n",
+       "      <td>345</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Remove non-nM activities</td>\n",
        "      <td>134</td>\n",
-       "      <td>110</td>\n",
-       "      <td>210</td>\n",
-       "      <td>345</td>\n",
+       "      <td>109</td>\n",
+       "      <td>208</td>\n",
+       "      <td>343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Only human entries</td>\n",
        "      <td>122</td>\n",
-       "      <td>97</td>\n",
-       "      <td>178</td>\n",
-       "      <td>299</td>\n",
+       "      <td>96</td>\n",
+       "      <td>176</td>\n",
+       "      <td>297</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Get minimum IC50 per molecule-target pair</td>\n",
        "      <td>122</td>\n",
-       "      <td>97</td>\n",
-       "      <td>130</td>\n",
-       "      <td>215</td>\n",
+       "      <td>96</td>\n",
+       "      <td>129</td>\n",
+       "      <td>214</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
@@ -8091,19 +8082,19 @@
       ],
       "text/plain": [
        "                                     filtering_step molecules targets assays  \\\n",
-       "0                        ChEMBL bioactivity entries       135     111    212   \n",
-       "0                          Remove non-nM activities       134     110    210   \n",
-       "0                                Only human entries       122      97    178   \n",
-       "0         Get minimum IC50 per molecule-target pair       122      97    130   \n",
+       "0                        ChEMBL bioactivity entries       135     110    210   \n",
+       "0                          Remove non-nM activities       134     109    208   \n",
+       "0                                Only human entries       122      96    176   \n",
+       "0         Get minimum IC50 per molecule-target pair       122      96    129   \n",
        "0               Only \"active\" molecule-target pairs        82      61     82   \n",
        "0                        Only molecule-kinase pairs        80      59     80   \n",
        "0  Only molecule-kinase pairs with activity <= 5 nM        18      18     21   \n",
        "\n",
        "  activities  \n",
-       "0        347  \n",
        "0        345  \n",
-       "0        299  \n",
-       "0        215  \n",
+       "0        343  \n",
+       "0        297  \n",
+       "0        214  \n",
        "0        138  \n",
        "0        136  \n",
        "0         27  "
diff --git a/notebooks/kinfraglib/figures/combinatorial_library_most_similar_chembl_molecules.pdf b/notebooks/kinfraglib/figures/combinatorial_library_most_similar_chembl_molecules.pdf
index 428e7f73..10563e30 100644
Binary files a/notebooks/kinfraglib/figures/combinatorial_library_most_similar_chembl_molecules.pdf and b/notebooks/kinfraglib/figures/combinatorial_library_most_similar_chembl_molecules.pdf differ