Finally, we construct a new data set consisting of six of the eight protein targets from the DUD-E Diverse Subset, for which ligand binding affinity data were available in the ChEMBL database, version 25, . We find that our models perform well when trained and tested on docked poses of ligands for a single target; but poorly when trained and tested on docked poses for different targets. While there is clearly some way to go in order to create truly generalised machine learning scoring functions, our results suggest that the inclusion of additional ligand-based input features in a scoring function helps to correct for potential errors introduced by docking. This thus greatly expands the set of targets and ligands that can be used to train new scoring functions, and will help to generate better predictions of protein-ligand binding affinities.