We split the dataset into train, validation, and test sets according to both protein pocket and ligand similarity scores. To compute pocket similarity, we used a pocket-level version of the TM-align algorithm 40 (details in the Supporting Information). Since a pocket may have multiple 3D structures, we defined the similarity between two pockets as the maximum pocket-level TM score between any two of their structures. The ligand similarity was defined as the Tanimoto similarity of two ligands' 2048-bit Morgan fingerprints with radius 3. Two pockets were placed in the same cluster if their TM scores were greater than 0.89. The TM cutoff was lowered to 0.82 if the pockets were known to bind to any ligand with Tanimoto similarity greater than 0.7. The rationale for choosing these two cutoff values is laid out in more detail in the Supporting Information. We split the dataset according to these pocket clusters. To ensure we can use performance on LIT-PCBA as an evaluation metric, we also ensured that all pockets in the same cluster as any LIT-PCBA target were also in the test set.