To further evaluate our recommended composite methods in realistic settings, we performed a thorough assessment of three QM recipes that emerged as especially promising from the benchmark results. Each uses DLPNO-CCSD(T)-F12d/cc-pVDZ-F12 single-point energies, but differs in its geometry optimization protocol: GFN2-xTB for rapid screening, B97-1/6-31G(D) for day-to-day applications, and ωB97X-D/def2-SVPD for enhanced accuracy. We gauged the performance of these approaches through both cross-validation and independent test-set evaluations, thereby capturing their effectiveness under practical conditions. Figure The MAEs from leave-one-out cross-validation exceed training errors by only 0.1-0.5 kcal/mol, and methods that perform best under cross-validation generally maintain their advantage in the test sets. This relatively small difference in errors and the positive correlation in performance justify the use of training-set metrics for model chemistry comparisons, especially given the computational intensity of performing full cross-validation on over 400 refits of the BACs per 284 levels of theory. All three examined methods exhibit strong performance for neutral singlets, indicating their reliability for typical organic molecules. However, errors increase substantially on the more challenging test set, which contains triplet and zwitterionic species. This disparity is unsurprising, given that only 7 triplets and 17 zwitterions are represented in the training data.