For the external LO datasets, we observe that the addition of drug discovery LO datasets in training improved the performance of 3DGNN and 2DCNN architectures while PIGNet and 3DCNN models showed reduced performance compared to the original models trained only with PDBbind data (Table ). The 3DGNN model showed the best performance with an average EF at 1% of 9.0 followed by 2DCNN with 7.3 and GLIDE with 6.6. However, performance of the models is not homogenous among the different datasets as shown in Figure . The 3DGNN appears to be the most consistent method by ranking best in six of the twelve datasets, second in five and third in one of the datasets (Table ). Interestingly, GLIDE performed admirably and was best for three of the LO target datasets while being last or penultimate ranked for five of the targets. Particularly, these targets represent examples of structures with challenging docking conditions. For example, the binding site of PDE2 and PDE10 contain two metallic cations and KDR shows flexibility across the structures in one of the loops forming the binding site. Table . Average enrichment factor performance metric for the retrieval of actives for the twelve drug discovery LO test datasets. The enrichment factor is the number of active compounds retrieved in top 1% predictions compared to random and averaged for each model architecture over all the drug discovery target datasets. Models were trained with and without LO datasets. MW bias. One critical issue we considered is the molecular weight bias. MW is a simple property that can show a low positive correlation with affinity and models tend to shortcut learning by overweighting the importance of MW or a correlated feature (number of atoms, number of interactions). To know if our models trained with the drug discovery LO data are providing enrichment in different MW ranges, we stratified the data by MW and computed the enrichment factor. Surprisingly, our models did not show a bias in enrichment towards high MW ranges.