Traditionally, conformer generation algorithms have adopted either a systematic or stochastic approach. Systematic approaches attempt to enumerate all reasonable values for rotatable bonds, and thus often have difficulty scaling. Stochastic approaches use random sampling to make the search process more scalable. Distance geometry 9,10 uses bond length, angle and other, possibly knowledge-based, 11 constraints to constrain the stochastic search space. More recently, machine learning has been used to either generate conformations directly or otherwise assist in the generation process (e.g., torsional sampling). Although there are multiple ways to evaluate the quality of a conformer generator, 1 most relevant for structurebased drug discovery is the ability to produce a bioactive conformation. That is, a conformation close to the conformation found in a protein-ligand complex should be generated, even if it is not the lowest energy conformation. Both free and commercial conformer generators were evaluated for this task and, provided that a sufficiently large ensemble is generated, most approaches succeed at identifying a low RMSD (< 2 Å) conformation. In particular, RDKit, which uses a stochastic distance geometry based approach combined with experimental torsional-angle and ring geometry preferences (ETKDG), 11 consistently performs as well as or better than other approaches.