We also checked whether training on multiple docked poses for each ligand affected performance on the PDBbind Test Set. For this we focused on the HB model as it consistently outperformed the LB and SB models in our previous experiments. We repeated the above experiment for the HB model, this time using all of the docked poses we generated for each ligand in the PDBbind Training Set, as described earlier. In contrast with the cross- experiment, training on multiple docked poses for each ligand significantly reduced the performance of the HB model on the PDBbind Test Set (Supporting Information Figure ). To understand how training on multiple poses for each ligand affects the HB model, we examined the relative importance of the features used by the RF. We found that when multiple poses for each ligand are used for training, ligand-based features become more important than when only a single pose is used in training (Supporting Information Figure ), suggesting that using multiple poses for each ligand introduces additional noise into the structure-based features that causes the RF to rely more on ligand-based features.