Structure-based virtual screening aims to identify compounds that bind to a pocket in a target protein structure by ranking compounds from a large chemical library according to predicted binding scores. The top-scoring compounds are prioritized for experimental validation. Traditional algorithms generate and score possible binding poses for each compound using molecular docking and physics-inspired heuristics. These docking methods, however, have limited accuracy. Moreover, their slow speed presents challenges for screening new libraries containing billions of compounds. Recently, deep learning techniques have been introduced to the field of structure-based virtual screening to accelerate both pose generation and scoring of protein-ligand complexes.