To address the latter point, in conf3 we therefore combine the higher accuracy of GFN1-xTB and the computational speed of GFN-FF: A conformer ensemble is generated with CREST at the GFN-FF level, while a subsequent local relaxation and energetic re-ranking is carried out using GFN1-xTB. Comparing again to conf1, we see a significantly better agreement between the methods (see Fig. ), with 5 remaining outliers falling beyond the error margins of ± 20 meV. It should be noted, that conformer searches are in general a difficult global optimization problem, which cannot be solved deterministically in an efficient manner. Therefore, some amount of uncertainty is unavoidable and will affect the ML models in all cases. As discussed in the following, achieving lower uncertainty at this stage leads to significantly lower predictive errors, however.