This example is a good illustration of the incorrect experimental datapoint effect onto the prediction model performance. RT prediction models that can be used beyond the specific conditions under which the training set was collected are of particular interest. Some previous works applied an additional post-training step for this purpose, other proposed to calibrate the predicted RT using reference compounds. Since both METLIN SMRT and EnamineRT datasets were obtained from similar LC-MS experiments (acidic conditions), we expected that their RT values would correlate, and that the model built on one dataset would be applicable to the other. 1,321 molecules appeared in both Enamine stock (screening collection, buildings block catalog 37 ) and METLIN SMRT (overlapped (OL) set). Therefore, we predicted the RT values for the OL dataset using our final model based on EnamineRT and the mean value of the experimental condition parameter (ECP).