We generate RDKit conformers using the ETKDG version 3 10,11 method of RDKit using default values and version 2022.03.1. This method combines distance geometry 9 sampling with knowledge based potentials to increase the efficiency of the algorithm without loss of accuracy (see Friedrich et al. and Figure ). To generate DMCG 12 conformers we use the pre-trained model Large Drugs/checkpoint 94.pt with the recommended settings for drug-like molecules. We strip the input SMILES strings of stereochemistry information, as this information is often missing in virtual screening datasets and we want to evaluate the ability of conformer generators to sample appropriate geometries (Figure shows the relatively small contribution of stereochemistry in our evaluations). For both generators we evaluate further refining generated conformations using the UFF molecular force field 36 as implemented by RDKit and default convergence criteria.