Here, we describe a new approach to compensate for potential errors introduced into protein-ligand binding affinity prediction through the use of docked (instead of crystallographic) poses. Previously, we reported that the inclusion of pose-independent ligand-based features improved the performance of several machine-learning scoring functions in predicting protein-ligand binding affinity when trained on crystallographic binding modes of the PDBbind database, and tested on the Comparative Assessment of Scoring Functions (CASF) benchmarks. We also found that ligand-based features are predictive of the average binding affinity of a ligand for its protein targets in PDBbind . Motivated by this, and the fact that ligand-based features are not affected by errors introduced by the use of docked poses, we investigated how the inclusion of a rich set of ligand-based features as inputs to a machine learning scoring function affects its performance when trained and tested on docked poses. Firstly, we investigated the effect of training and testing scoring functions using only docked poses generated by Smina, a fork of AutoDock Vina, as opposed to X-ray crystallographic poses, on the resulting scoring functions. We found that pose prediction errors are common when re-docking the ligands in the PDBbind database into their corresponding protein co-crystal structures, and that even when accurate poses were generated, the Smina scoring function often failed to rank these native-like poses higher than non-native ones. We also found that training and testing machine learning scoring functions on docked poses degrades scoring function performance relative to those trained and tested on crystallographic poses even when there is a high level of similarity between proteins in the training and test sets. Augmenting the input features with rapidly computed ligand-based molecular descriptors results in a greater gain in performance when using docked poses, than when using crystal poses for training and testing.