We used interface fingerprints (IFPs) as the backend ML model's input. In contrast to molecular fingerprints that encode the chemical topology of molecules alone, IFPs capture the geometric and biophysical properties of the full protein-ligand interface. They have been used to predict docking scores and binding affinities and help address activity cliffs in ligand-based machine learning scoring functions. Fingerprint representations perform comparably to more complex message-passing neural networks at small dataset sizes. The ligand and interactions are divided into multiple nested spherical shells centered on each ligand atom to convert the calculated interaction graph into a fixed-size vector representation of the protein-ligand interface. The first shell at radius 0 contains the initial atomic identifiers for each ligand atom, including their "invariant" properties. These invariants can be explicit, including the number of non-hydrogen neighbors, total bond order, atomic number, atomic mass, atomic charges, and number of attached hydrogens, or functional/pharmacophoric (aromatic, acceptor, donor, hydrophobic, etc). EIFP and FIFP describe these explicit or functional interaction fingerprint approaches, respectively. Progressively larger shells centered on the atoms or atom-group centers of mass contain the interactions within their boundaries.