We curated a dataset of ligands from the Cambridge Structural Database (CSD). We separately identified candidate hemilabile ligands from this CSD set and then used the frequent occurrence of a single, consistent denticity to assign ligands as having a high probability of being nonhemilabile. We trained machine learning (ML) procedures to predict ligand hemilability and used feature analysis of the trained models to show why common heuristic rules can struggle to fully account for hemilabile character. This ML model allowed us to further expand and suggest a set of candidate hemilabile ligands from existing (i.e., in the CSD) and thus synthesizable ligands.