CREST generates large ensembles which are of better quality than those generated with algorithms using force fields or knowledge based algorithms, but the generated geometries should still be reoptimized at a level of theory higher than the GFN2-xTB which CREST uses before any computation of properties is done. Unfortunately, for large systems, the computational cost for reoptimization of the full ensemble (often hundreds or thousands of geometries) using density functional theory (DFT) can be enormous. As a result, in our previous work, we have reoptimized only the lowest-energy geometry for large systems (hundreds of atoms) or a low-lying fraction of the ensemble (often the lowest 2 kcal/mol, instead of the full 6 kcal/mol) for medium-sized systems (50-100 atoms). The problem with such an approach is that those geometries which are lowest in energy using the GFN2-xTB method, upon which CREST depends, are not necessarily the same as the geometries which are lowest in energy when using DFT. Thus, choosing the structure or structures which CREST reports as being lowest in energy is no guarantee that all the true low-energy structure(s) will be found. Indeed, the original CREST paper 14 indicated that the ranking of conformers using GFN2-xTB often differs significantly from rankings using DFT, and the usual procedure among users of CREST is to reoptimise ensembles at some higher level of theory. Additionally, when CREST-generated ensembles are reoptimized using DFT, many of the conformers, up to 90% of them in some cases, coalesce to become equivalent, indicating that the GFN2-xTB potential energy surface may contain many spurious minima. Recently, a promising new set of methods has been introduced to computational chemistry: the 3c composite methods. These use either Hartree-Fock or DFT with a very small basis set, carefully curated for maximum error cancellation, plus two or three empirical corrections. Some recently introduced 3c methods include HF-3c, 29 B97-3c, 30 r 2 SCAN-3c, PBEh-3c, and ωB97X-3c. In principle, and in practice, reoptimizing ensembles using one of the 3c methods will significantly reduce errors in energy and geometry present in CREST-derived ensembles, and will also allow for the elimination of most of the spurious minima. Therefore, in this work, we explore re-optimizing and re-ranking the ensembles using 3c methods before choosing low-energy structures to reoptimize using DFT. Because the 3c methods are much less computationally expensive than DFT, this should result in greatly reduced computational effort compared to reoptimizing full ensembles using DFT, while much more reliably finding the correct low-energy structures for DFT optimization.