<p>Understanding protein hydration at the atomic level is a hallmark challenge in deciphering ligand binding and guiding ligand discovery. The fact that 95% of deposited crystal structures are cryogenic currently limits the utility of crystallographic water features, because water networks change with temperature. Here, to increase our confidence in interpreting cryogenic water networks, we developed ColdBrew—a machine learning method that predicts the likelihood of cryogenic crystallographic water molecules appearing at room temperature. To link ColdBrew probabilities to water displaceability, we examined &gt;1 million waters in ligand-bound cryogenic structures and found that ColdBrew sufficiently separated conserved waters from displaceable or absent ones. To link our metric to water energies, we applied inhomogeneous solvation theory to show that waters with high ColdBrew probability and low displaceability indeed have more favorable energies. Unlike expensive computational methods, ColdBrew provides a scalable approach to assessing the utility of water molecules for ligand discovery directly from experimental cryogenic crystal structures. We precalculated ColdBrew probabilities for &gt;46 million waters in over 100,000 Protein Data Bank structures.</p>