Next, to further examine how learning receptor conformational diversity can help AtomNet PoseRanker recognize valid binding modes, we developed a protocol that creates an ensemble of computationally sampled receptor conformations for docking based on Rosetta's comparative modeling protocol 46 (Methods). Aside from providing conformational variability when multiple experimental structures are lacking, computationally sampled ensembles offer advantages over those constructed from experimental structures for a pose training task, including removing variations due to mutations or differences in construct; normalizing the numbers and sources of different conformations available for each protein in the dataset; and permitting the trained model to be used in practical applications to docked poses to homology models produced through Rosetta-based pipelines.