On the one hand, fixing instrument model, column type, and chromatographic method significantly reduces the number of variables that must be taken into account to predict retention times. This simplifies the prediction workflow and enables deeper study of the relationship between chemical structure and observed retention time. On the other hand, this approach greatly narrows the range of experimental applications. In order to use predictive models trained on the METLIN data under different experimental conditions, a whole other dataset of retention times must be compiled and transfer learning techniques applied. This strategy is applicable, but each new set of experimental conditions requires a re-training stage, which is a severe limitation.