Representing chemical compounds in a machine-readable format is considered a challenge in chemoinformatics due to its effect on different surrogate models and, thus, their predictive performance. Previous literature has led to ambiguous outcomes on whether the addition of chemical information within low data regimes, such as the initialization of active-ML search strategies, is beneficial. To learn more about featurization effects on this specific application, we compared two input feature sets. The large feature set contains information on the components' concentrations, component pKa values, number of protons a buffer can accept/donate and the initial pH value of the buffer mixture (prior to any acid/base addition).