Most molecules exhibit some level of conformational flexibility, the existence of multiple lowenergy geometries that differ mostly by changes in the torsional angles of both acyclic and ring bonds. Many methods have been developed to sample conformations, with benchmarks frequently focusing on finding one geometry close to an experimental crystal structure. Consequently, most conformer sampling methods derive torsional angle distributions from experimental crystallographic data -not only to provide geometries close to such benchmarks, but also as large diverse repositories of "ground truth" geometric properties such as bond lengths, angles, and dihedrals. One challenge is that experimental crystallographic data are limited by the size of the data source and reflect some inherent biases. In order to be collected, the molecules must be stable, synthetically accessible, and actually made and crystallized. While new cryo-electron microscopy (cryo-EM) techniques are improving dramatically and have less stringent requirements on crystals, generally growing a high quality crystal for small molecule crystallography is a time-consuming process. Moreover, it is known that compounds with experimental crystal structures are generally smaller and exhibit fewer conformers than other compounds. Similarly, compounds containing elements outside the common organic subset (e.g., B, As, Se) or less common chemical motifs may be poorly represented in experimental crystallographic databases. Also, even for compounds found in an existing database, much chemistry is performed in solution and gas phases, where solid-state preferences may not directly apply. Finally, several works have noted challenges with deriving data from some crystallographic databases. Consequently, finding unbiased alternative sources of accurate and reliable torsional angle preferences could significantly expand the use of conformational sampling to new chemical space. Typically, sampling has been performed using small molecule force fields (e.g., UFF ), which have shown limited fidelity compared to density functional and other first principles quantum chemical methods. The development of efficient dispersion-corrected semiempirical methods such as GFN2-xTB, as well as new machine learning methods such as ANI and OrbNet, offer improved accuracy of torsional angles and non-bonded interactions with moderate computational cost. Moreover, several large-scale computational efforts including PubChemQC and the QCArchive have provided large amounts of optimized gas-phase theoretical geometries using high-quality density functional methods.