We note that while a NES approach yields increased accuracy, this comes at a significant computational cost. For an initial, large-scale pK a scan, a conventional in silico predictor (e.g., PypKa, PropKa, MOE, etc.) may be the more cost effective option, which could then be followed by a more accurate MD-based approach. With the aim of identifying reactive cysteines, an initial predictor should not disqualify any true reactive cysteines, even if it means introducing a larger fraction of false positives. The number of false positives introduced should be kept minimal, otherwise the computational benefit of the predictor would be small. While all methods attain relatively high precision (i.e., > 0.90), PypKa and PropKa only attained recalls near 0.30, compared to 0.87 for CHARMM36m-ECC, which reduces the usefullness of these methods as initial screening tools.