Using the developed classification models implies that for screening RPLC samples against databases such as SusDat, 1/5 of the overall time can be saved, which becomes even more significant when applying it to larger sample sets. Additionally, this will result in higher confidence identifications when performing database matching for an RPLC NTA method with SusDat, by reducing the overall number of potential candidates and thus false positive identifications.