The semi-empirical GFN1-xTB and GFN2-xTB methods show the largest RMSDs, with values of 0.34 and 0.44 Å, respectively. Hence, based on these findings, if a semi-empirical method should be used in the funneling approach, GFN1-xTB is a better choice than GFN2-xTB. This is consistent with recent benchmark studies and shows that the trend follows here. The empirically corrected DFT methods PBEh-3c, B97-3c, and r 2 SCAN-3c perform well with RMSDs of 0.18, 0.11, and 0.09 Å, respectively. This indicates that B97-3c and r 2 SCAN-3c could be good choices for obtaining accurate geometries at a lower cost. Interestingly, the two other DFT methods, PW91 and M06-2X, are in all cases worse than the DFT-3c methods, even when using large basis sets. Employing the larger 6-311++G(3df,3pd) basis set with the ωB97X-D functional yields a very similar geometry compared to utilizing the smaller 6-31++G(d,p) basis set. This further illustrates that the geometries are not that dependent on the employed basis set, but more on the functional.