The same datasets used to train GNINA's CNN models are also used in this work. Three datasets are used to impart the CNN models with different expertise. The PDBbind General dataset v2016 consists of 11,324 protein-ligand complexes with experimentally determined binding affinity data and 201,839 generated binding poses. The CrossDock2020 v1.3 dataset 7 contains cognate protein-ligand complexes and non-cognate (crossdocked) proteinligand complexes. This dataset includes a total of 22,566,449 binding poses, with proteinligand poses being at least 0.25 Å RMSD from any other pose in the same pocket to provide a diverse set of poses for training. The updated version 1.3 addresses ligand and receptor misalignment problems and incorrect bond typing problems present in earlier versions. Finally, we used the Redock v1.3 dataset, a subset of the CrossDock2020 v1.3 dataset which removes all non-cognate ligand docked poses and consists of 550,562 generated binding poses.