In a field in which progress has been hampered by the chronic lack of measured data, active learning opens new avenues for progress, in particular, because of its capacity to provide guidance to experimentalists in their time and resource-intensive efforts to increase the knowledge of the metabolic stabilities of atoms in their specific environments. Instead of aiming to annotate all atoms of as many molecules as possible, the active learning approach pinpoints experimentalists to the atom positions that are most informative to the model and, hence, most rewarding to annotate. The application of active learning during the acquisition of experimental data is expected to result in more accurate and informative data.