We find that removal of test-set similar proteins and ligands from the training sets degrades performance but does not abrogate the predictive power of ligand-based features. Furthermore, we show that a model using only ligand-based features appears to be predictive of the mean affinity of a ligand for its binding partners when trained and tested on PDBbind data. We computed the relative importance of the features used by each model and show that when structure-based and ligand-based features are combined, both structure-based and ligand-based features are among the top-ranked features. These results suggest that quickly-computed ligand-based features should be used to improve the ability of a machine learning scoring function to predict proteinligand binding affinity.