In the present work, discrepancies between optimized geometries from different force fields highlight potential issues, but we have no ground truth or point of reference for sorting out which geometries are correct and which are not. This data simply helps us select molecules/chemistries which may be informative, and prioritize them for further study. Particularly, ideally one might generate optimized geometries for these same molecules with QM calculations and then use these to help assess which force fields produce the best results, or use these in force field training sets to improve force field quality. Indeed, informative molecules from the present study are being used for precisely that purpose. Particularly, a subset of the FivePlus set was used as the basis for the "coverage" set used for the first OpenFF Parsley release, OpenFF 1.0 . A larger portion was used in benchmarking OpenFF 1.0. Then, for OpenFF 1.2, training data was completely redesigned, in part drawing from what was called the "eMolecules Discrepancies Set" , corresponding to the first portion of the FivePlus set generated here. This training data redesign resulted in improved performance on a variety of benchmarks . The relevant optimized geometries are freely available in QCArchive as part of the OpenFF 1.2 training and benchmarking datasets.