Rowan's pK BHX prediction workflow uses neural network potentials to accelerate geometry optimization, requires only a single low-cost DFT calculation per molecule, and gives highaccuracy results across a wide set of structurally varied molecules and medicinally relevant scaffolds. We illustrate the utility of these predictions with a variety of case studies drawn from the medicinal chemistry literature.