We found that when structure-based and ligand-based features are combined, both structure-based and ligand-based features were ranked highly, and that the same ligandbased features are ranked highly regardless of which structure-based features they were combined with. This suggests that the same information is consistently extracted from the ligand-based features and that this information is not redundant with that provided by structure-based features. Our results suggest that even under stringent validation, the addition of a diverse, quickly-computed set of ligand-based features to a scoring function yields improved predictions of binding affinity.