To reduce the effect of data biases, data augmentation using inactive compounds with similar physicochemical properties have been included in training. Furthermore, we included pose decoys where active molecules in incorrect bound complexes were used for training. While both of these efforts are expected to improve model generalization by forcing the model to focus more on the interactions or learn that the features of the ligand alone are not responsible for the activity, it may be introducing noise via a 'virtual binding' mode and an assigned low pX50 (normalized between 0 and 3 in this work). From the DL architecture perspective, recent efforts make the models more generalizable by limiting learning to just interaction information, thus avoiding overfitting on the ligand, the protein or both. For example, Volkov et al. recently analyzed the problems related to the PDBbind dataset and DL models, concluding that for prospective applications, a model trained using interaction data might be more generalizable. However, the analyses performed in their work We have addressed the data sparsity problem by augmenting the data using drug discovery LO datasets and a rigorous docking protocol to ensure that resulting complexes are as close as possible to their hypothetical crystal structures. Interestingly, we unexpectedly observed an increase in PDBbind core set testing performance when training the model with drug discovery project LO data in comparison to training using only PDBbind data. More importantly we observed modest correlation for some of the external LO datasets where models trained with only PDBbind or GLIDE showed no trend. Interestingly, for the internal validation test sets the models performed rather poorly and similar in all cases but for the LO external test sets performance was also poor in terms of correlation of predicted and experimental activity but there was an improvement when using internal data. It must also be noted the increased difficulty of obtaining correlation for LO datasets where the distribution of activities across the activity range is imbalanced with most activity points concentrated in 2 to 3 pIC50 units. Considering the correlation between the predicted and experimental activity the overall performance was unclear if the DL models were outperforming docking. It is notable that docking (using GLIDE here) often performs well in the respective tests performed throughout our study. This contrasts with some reports where DL models often substantially outperform docking. However, upon close inspection, some of those studies use docking inappropriately, for instance to find a site in addition to the binding mode. As mentioned in the introduction, site identification was not the purpose for which docking algorithms were developed. Here, the docking performance is likely tending towards a better or more typical use case scenario because we are filtering to only retain the best scored, most reliable poses that may also conserve an MCS or certain interactions.