Given the power of the ligand-based features, we investigated their predictive ability for ligands that bind to multiple targets and found that the predicted binding affinity of a model using only ligand-based features was strongly correlated with the mean of the experimental protein-ligand binding affinity of a ligand for its binding partners. This correlation remained strong when ligands with a Tanimoto similarity of greater than 0.9 to the test ligand were excluded from the training data. This correlation gradually weakened when progressively less similar ligands were also excluded, suggesting that while the model's predictions are not reliant upon memorization of previously-seen highly-similar ligands, it does not extrapolate well to completely novel ligands.