The ReaLigands library reported here represents an important tool for computational catalyst design using quantum-chemical structure and energy calculations. This >30,000 ligand library was generated by dismantling ligands from experimental crystal structure followed by classification based on denticity. A Random Forest machine learning model was used to make ligand charge assignments. PCA and UMAP analyses provided a glimpse at the diversity and range of ligand properties. We demonstrated how this library can be used in conjunction with automated building programs to evaluate a large collection of ligands for barrier heights. Perhaps most important, catalyst designs based on ligands from this library have a direct connection to experiment that will likely facilitate translation to the lab, which is the goal of computational-based catalyst evaluation.