The performance of the scoring functions using each feature set when trained on different versions of the PDBbind refined set with no data removed is shown in Fig. , while Fig. shows corresponding results when structures whose ligands have a high Tanimoto similarity to any ligand in the test set are removed from the training set. In both of these cases there is no consistent improvement in performance when larger, more recent versions of the refined set are used. This is contrary to the results of Li et al. and may suggest that, at least when using RF to train an estimator, there is an element of 'learning saturation', beyond which there is limited benefit to using additional training data. However, for a given release of PDBbind, a scoring function trained on the general set outperforms the same scoring function trained on the refined set (Supporting Information Fig. -13), consistent with the findings of Li et al.. This difference in performance vanishes for all scoring functions when structures with testset similar ligands are excluded from the training set, and is greatly reduced when structures with 90% protein sequence identity to those in the test set are excluded. This suggests that the increase in performance when training on the general set can be attributed to increased representation of the core set proteins and ligands in the training data.