Quantifying the accuracy of property prediction under realistic conditions is challenging: evaluation over a large dataset, while superficially appealing, often leads to exaggerated correlation coefficients that are not reproduced in smaller, less diverse populations. Instead, we follow Rich and Birnbaum in using "assay-stratified metrics" to evaluate the predictive ability of our pKa workflow. All datasets were scored using four metrics: mean absolute error (MAE), root mean square error (RMSE), the square of the Pearson correlation coefficient (r 2 ), and Kendall's tau (τ). Low values for MAE and RMSE reflect a high degree of absolute accuracy, while high values for r 2 and τ demonstrate relative accuracy within a given dataset (arguably more predictive of performance under real-world usage).