The latest machine learning models have not been evaluated for their ability to generate bioactive conformations. Instead, they are mostly trained and evaluated on the GEOM dataset, which contains 37 million conformers of more than 450,000 molecules with the goal of accurately representing, at the level of semi-empirical density functional theory, the vacuum conformer-rotamer ensembles of these molecules. Deep generative models significantly outperform RDKit at this task, but an extended sampling and clustering approach using RDKit achieves highly competitive performance. It is not clear that it is a fair comparison to compare methods that utilize different amounts of sampling, 30 so here we evaluate RDKit and a deep generative model using identical sampling and ensemble formation criteria. As the Direct Molecular Conformation Generation (DMCG) 12 was found to perform best at the task of reconstituting the ensembles of the GEOM-Drugs subset of GEOM, we evaluate it here at the task of bioactive conformation recovery. However, our main goal is not to extend previous evaluations but to explore the impact of various choices made in the conformer generation process, such as the size of the ensemble, the criteria for including conformers in the ensemble, and the use (or not) of energy minimization, has on the ultimate endpoint of the common structure-based tasks of pharmacophore search and molecular docking.