Recent work has expanded the knowledge on ML performance for predicting and ranking thermally accessible conformations. Though ML was not tasked with large bond stretches as in this work, the ability of ML methods to rank conformational energy was only comparable to that of semiempirical methods. While this is not equivalent to the accuracy of density functional (DFT) or ab initio electronic structure methods, for ML methods to be an accurate surrogate for quantum chemical methods, continued advancements in ML models and training sets are needed to provide further performance improvements.