To gain a better understanding of the influence of these sampling methods on the conformational search of large drug-like molecules, we analyzed the structural and energetic data of conformers corresponding to 18 randomly selected compositions, each containing approximately N = 50 atoms. Fig. (a) displays the output for the variation of the averaged number of clusters, denoted as ⟨M⟩, as a function of the root-mean-square deviation, ∆R, among conformers that constitute a cluster. This calculation was first done per molecule and then averaged over the 18 cases. This showed that more diverse conformers become part of the same cluster when ∆R increases, resulting in a reduction of the number of distinct clusters. The decrease in ⟨M⟩ for Maestro and Omega is faster compared to CREST, which may indicate that conformational search workflows based on distance geometry methods are insufficient for probing the PES of more flexible molecules. However, thanks to the random ensemble option for conformer generation, RDKit produces a larger ⟨M⟩ than CREST for ∆R > 1.0 Å. To examine this result further, we compute E AT and E MBD for a set of representative conformers per molecule (see selection criterion in "Methods') using DFTB3+MBD level of theory. Notice that the resulting total number of conformers depends on the code employed for their generation, i.e., CREST → 3747 conformers, Maestro → 100 conformers, Omega → 204 conformers and RDKit → 1872 conformers. Thus, despite having fewer clusters at ∆R = 1.5 Å, conformers generated by CREST code show a better coverage of the energetic space defined by DFTB-E AT and DFTB-E MBD (see Fig. ). Additionally, it becomes evident that the CREST sampling method can generate conformations of increased complexity, characterized by a more folded structure and a stronger dispersion interaction, as illustrated with the inserted structures in Fig. ). This underscores the relevance of taking vdW interactions into account when generating and identifying conformers of large drug-like molecules. Similarly, this provides compelling evidence of the efficient conformational search workflow implemented in CREST code, which improves coverage of both conformational and property molecular space. It is noteworthy that the calculations executed by the CREST code incurred higher computational expenses compared to those carried out by chemoinformatics codes.