While the design space of possible TMCs is immense, computational strategies provide a means to accelerate the discovery process by rapidly evaluating large samples of proposed complexes. Such high-throughput virtual screening (HTVS) campaigns typically rely on density functional theory (DFT) to solve for the approximate electronic structure of a given complex. Different TMCs may be compared in terms of DFT-calculated molecular properties, including their geometries, relative energies, and frontier orbital energies, to name a few. Although DFT alone is too slow to enable exhaustive sampling of the TMC design space, it may be combined with more rapid machine learning (ML) approaches to accelerate the process of rapidly screening TMCs and identifying promising candidates to guide experimental synthesis. A key challenge with this integrated HTVS approach is that DFT calculations are reliant on accurate three-dimensional initial structures of the systems to be analyzed, while ML models are often sensitive to the two-dimensional connectivity between the metals and ligands, both of which are difficult to determine a priori. Experimental datasets can provide meaningful insights regarding trends in structures and properties of synthetically-accessible TMCs. The Cambridge Structural Database (CSD) is a digital repository of crystal structures from X-ray and neutron diffraction of experimentally synthesized complexes, and is one such large dataset that has been used extensively to study TMCs. Large datasets of transition metal complexes and their constituent ligands have been curated, aided by the development of software designed for enhanced structure generation and parsing of crystallographic data. Such datasets have been used to train ML models for accurately predicting electronic properties of TMCs in the hopes of generalizing well to unseen complexes and guiding future design efforts. While prior work has involved dataset curation and model development for property prediction, these efforts have largely been focused on TMCs with known metal-ligand connectivity. One exception is accurate models that were developed based on CSD structures to identify when a ligand may exhibit multiple coordination modes. Most efforts centered exclusively on predicting ligand properties typically assume a single known metal-ligand coordination, leaving researchers with no accurate way to determine how a novel, unseen ligand will coordinate in a TMC without resorting to expensive and time-consuming experimental characterization techniques.