As in McNutt et al. , trained models are evaluated on two tasks within the GNINA molecular docking pipeline: redocking and crossdocking. Redocking is evaluated using the PDBbind Refined v.2019 dataset; ligands smaller than 150 Da or larger than 1000 Da are removed, resulting in 4,260 protein-ligand complexes. Redocking evaluates the models performance when docking ligands to their cognate receptor structures. However, redocking performance is an unrealistic benchmark since most real-world docking is between new ligands and a non-cognate receptor structure. To this end, we perform a crossdocking evaluation using the dataset from Wierbowski et al. ; this dataset provides a benchmark to evaluate proteinligand docking without a cognate receptor structure. The dataset was filtered via the same size and parsing criterion as the redocking dataset. We used a downsampled subset of the whole dataset for evaluation, which contains 7,970 protein-ligand pairs. More details on the evaluation datasets and the filtering process is provided in McNutt et al. . GNINA's molecular docking time was benchmarked on a random subset of 100 complexes from the PDBbind Core set v2016. A list of the PDB IDs of this subset and the specifications of the benchmark are detailed in the supplement.