One of the main challenges of this project was finding a molecular representation that could uniquely describe a given molecule. For this purpose, we originally tried features from a method called "Geometric Scattering for Graphs", which provides truly index-invariant features by "scattering" atomic signals over the molecular graph structure, and storing summations of different moments of the scattered signals as features. We had showed previously that this approach could predict partition coefficients for small molecules to high accuracy. We attempted to use these GSG features by themselves, as well as GSG features computed with the Behler-Parrinello symmetry functions as signals, and none of these feature sets were able to generate proper models for Flexible Topology simulations. In these cases we found that minimizing the loss function was not sufficient to achieve the desired ligand structures. In other words, many structures were possible that were far from the target ligand structure, but still reproduced the features exactly. The current implementation, which directly compares atomic feature values after an on-the-fly Hungarian algorithm mapping step, was found to be superior to the graph scattering approach.