We were inspired by Balcell's tmQM database, and more recently the updated tmQMg database, that provides a set of >80,000 single transition metal complexes extracted from the Cambridge Structure Database (CSD). We realized that this database of structures contains most of the experimentally reported ligands mounted on a single transition metal, which if extracted and classified would provide an extremely useful starting point to computationally design new catalysts (Figure ). While this manuscript was in preparation a similar idea was advanced by Corminboeuf who reported the program cell2mol that converts crystal structures to individual molecules. Here we report the development of a modified depthfirst search algorithm for ligand identification and a machine learning based model using simple quantumchemical calculation features for charge assignment. The ligands were sorted based on direct ligand-tometal atomic connections and denticity. This experimentally based library of >30,000 ligands is called ReaLigands. To demonstrate some of the ligand categories and information provided by the ReaLigands library we used dimension reduction techniques to analyze representative tridentate ligand categories. To demonstrate the utility of this library for catalyst screening, we used this library in conjunction with our automated building and optimization tools to generate 750 bidentate Rh-H ethylene migratory insertion transition states and barriers.