McCammon 7 used both crystallographic and docked poses in the development and evaluation of their scoring function, NNScore 2.0, and noted that the optimal choice of docking protocol was highly system-dependent, but did not examine the difference in performance between training using only crystallographic or only docked poses of the same complexes. Zilian and Sotriffer 8 trained SFCScore RF using crystal poses from the PDBbind database, and validated on a combination of crystal poses from the PDBbind database and docked poses from the CSAR-NRC HiQ and the CSAR 2012 benchmarks. SFCScore RF demonstrated strong performance on crystallographic poses of the PDBbind 2007 Core Set, with a Pearson correlation coefficient between the predicted and experimental binding affinity of 0.779; and on docked poses of the CSAR-NRC HiQ test set, with a Pearson correlation coefficient between predicted and experimental binding affinity of 0.730. Performance on the CSAR 2012 benchmark was highly target-dependent, with the authors attributing poor performance on the kinase targets CHK1 and ERK2 to the poor quality of docked poses. More recently, Jimenez et al. validated K DEEP using both crystal poses and docked poses but, as in the earlier studies, used docked poses only when crystal structures were unavailable, so the impact of the use of docked poses in place of crystal structures was not determined. investigated how the use of docked poses in place of crystal poses affected the performance of the AutoDock Vina scoring function, RF-Score, and RF-Score v3. They reported that, contrary to what might be expected, using docked poses in place of crystal poses had only a small effect on the accuracy of their binding affinity predictions; for example, the Spearman rank correlation coefficient between predicted and experimental binding affinity attained by RF-Score v3 on the PDBbind 2013 Core Set dropped from 0.662 (crystal poses) to 0.633 (docked poses). The authors also reported that this drop in performance was reduced by training RF-Score v3 on docked poses instead of crystal poses, resulting in a Spearman rank correlation coefficient between predicted and experimental binding affinity of 0.643 on the PDBbind 2013 Core Set, suggesting that training on docked poses can make a scoring function less susceptible to errors when subsequently tested on docked poses.