We also evaluated the model performance on ChEMBL datasets that were compiled using the same pipeline utilized for the drug discovery project LO datasets. Although the best model was the 3DGNN trained with internal data, the addition of the LO data did not offer an improvement as substantial as seen for the internal LO test sets. This finding can be explained by two main reasons: data quality and differences in chemical space. The data quality issue is mainly due to the difficulty in finding congeneric series of compounds with a representative analogue in a public domain crystal structure. This challenge compelled us to use constrained docking, which can result in inaccuracies in the computed best pose. Moreover, the advantage of training with the LO data may not be as relevant for the ChEMBL data as for the LO datasets due to increased difference in the chemical space between the two data sources.