All three models (LB, SB, and HB) are strongly affected by the similarity between the training and test sets, with the exclusion of training set complexes with similar proteins or ligands to those in the test set significantly reducing performance. These results echo our earlier results and those of Su et al. , indicating that even when potentially lessaccurate binding poses are used, it is necessary to consider the effect of biases in the available structural data when training and evaluating models. The inclusion of ligand-based features in structure-based models always improves performance when using docked poses, and only ceases to improve performance when using crystal poses if the maximum fingerprint Tanimoto similarity between ligands in the training and test set is less than or equal to 0.5. This suggests that the inclusion of ligand-based features is a robust method of enhancing scoring function performance, particularly when using docked poses in both training and testing.