The majority of the data for HIVRT and CP3A4 span only four orders of magnitude, with pChEMBL values ranging from 4 to 8. The data for GCR cover a slightly larger range, with pChEMBL values ranging from 4.7 to 10, while the data for AKT1, HIVPT, and KIF11 span at least six orders of magnitude. The binding affinities all lie within the range of values represented in the PDBbind 2018 Refined Set, so a Random Forest (RF) model trained on PDBbind data could be expected to interpolate successfully. The distributions of the pChEMBL values for the six targets are shown in Supporting Information Figure . We performed three different validation experiments using the Updated DUD-E Diverse Subset. First, to explore the effect of using docked poses in place of crystallographic poses, we trained on docked poses (see "Docking Protocol") of the PDBbind Training Set and tested on docked poses of all of the ligands obtained from ChEMBL for each target. Second, we investigated whether our models could learn from docked poses obtained by docking ligands taken from ChEMBL into a single structure of a protein target, rather than using the co-crystal cognate structure of the protein each ligand. To do this, for each target we randomly selected 80% of the ligands obtained from ChEMBL for that target to use as a training set. The resulting model was then tested on the remaining 20% of the ligands for that target (so-called "Intratarget training"). Third, we investigated whether our models could generalise to ligands for a previously-unseen target when trained upon data other than the PDBbind Refined Set.