Varying the composition of the training set chronologically, by restricting to data available only up until a particular year, had little effect on affinity predictions. This suggests an element of learning saturation for the targets tested with the data currently available. We showed that, in contrast, excluding proteins from the training set that are sequence-similar to those in the test set has a deleterious effect on affinity predictions and that even excluding only those proteins with identical sequence to those in the test set leads to significantly reduced scoring function performance. We also showed that even when ligands with high Tanimoto similarity to those in the test set were excluded from the training set, the predictive power of the scoring functions was still increased by including ligand-based features.