While far from routine, quantum-chemical calculations, typically density functional theory (DFT), are now being used to design (or redesign) molecular catalysts. Recently there has also been a surge in uniting DFT calculations with machine learning and related data science approaches for catalyst predictions. The key to successful computational catalyst design is identification of a specific ligand framework mounted on a transition metal that will result in the desired reaction reactivity and/or selectivity. Additionally, for successful theory-to-experiment handoff designs should consider ligand synthesis in the context of previously prepared ligands. Stated another way, computational designs with highly exotic or likely-to-be unstable ligands are unlikely to be synthesized.