In contrast to the overall decent performance of DFT and wavefunction methods, semiempirical methods performed poorly on Wiggle150. We studied 4 semiempirical methods: Grimme and coworkers' semiempirical methods, GFN1-xTB and GFN2-xTB, and the older methods AM1 and PM6. When ranked in terms of MAE, newer methods outcompete older methods: GFN2-xTB > GFN1 > PM6 > AM1. However, when ranked in terms of RMSE, the order is exactly reversed-AM1 performs best, while GFN2-xTB performs worst. These data suggest that the additional complexity of newer semiempirical methods can lead to unreliable performance on strained structures very different from the structures these methods were optimized on. The two forcefields studied here both gave dismal results. The failure of Sage, a conventional forcefield similar to the Amber forcefields, is unsurprising: 2017 study by Kanal and co-workers argued that commonly used forcefields "should not be trusted" for conformer ranking, and that their predictions are "wholly unreliable for conformer screening." In contrast, the poor performance of GFN-FF is more unexpected: in their initial publication describing GFN-FF, Spicher and Grimme report that GFN-FF is "on par with some dispersion-corrected DFT methods" at describing relative conformer energies. Our results show that this is not true for the high-energy conformers studied here. The Pareto Frontier of Computational Methods. Many of the applications for which good performance on non-equilibrium structures is required, like automated reaction-path exploration and various molecular dynamics-based workflows, also require large numbers of calculations to be run. We compared the speed and accuracy of different methods, with the goal of identifying give worse accuracy and worse timing, and should be avoided for production use. We note that this study has not comprehensively surveyed many basis-set effects; we leave this substantial task to future work, with the observation that many commonly used basis sets may not be Paretoefficient. If faster methods than r2SCAN-3c are desired, AIMNet2 is the best choice by far. AIMNet2 offers comparable accuracy to B97-3c while running approximately eight orders of magnitude fasterfaster than every semiempirical method, and even faster than GFN-FF. This is dramatically different from the results reported by Folmsbee and Hutchison in their 2021 study: in that study, the ANI-2x ML model was similar to GFN1-xTB and GFN2-xTB both in terms of cost and accuracy. In our hands, both ANI-2x and AIMNet2 are significantly faster than xTB-based methods, and AIMNet2 is substantially more accurate, particularly in terms of RMSE. (We note that the present study did not employ hardware acceleration, and that the ML methods used here are likely to be even faster when GPUs are used).