HILIC Methods. Identical encoding schemes and filtering methods were utilized for HILIC methods as were for the RP methods, ensuring that methods had a maximum of three inflection points, binary solvent composition, and constant flow rate. The primary difference between HILIC methods and RP methods in our dataset is that gradients are typically described as starting at %B ≈ 100 % (i.e., the organic phase), with increasing contributions from the aqueous phase. This profile produces gradients that are effectively "upsidedown" compared to those encoded in our RP models (see Figure ). To maximize similarity to the gradient encodings in our RP models for the purposes of transfer learning, we invert the labels (e.g., A, B) for the mobile phase gradients such that the gradient starts at a %B minimum and matches the shape of the RP profiles. While this has the consequence of inverting the character of mobile phases A and B (viz. the organic and aqueous character), we believe that this difference is more easily learned as compared to a complete shift of all the gradient profile encodings. Additionally, in most of the HILIC methods, the organic phases (typically ACN) are not pure and contain 5-10% H2O. Since these concentrations are relatively consistent, we approximate the organic phase as 100% the organic solvent, for ease of encoding and because the small volume of water should be learned implicitly by the model. We found that including the concentration of the additives present in the mobile phase instead of just a one hot encoding had a large, positive effect in performance not observed for RP predictions. After filtering and consolidation, our HILIC library consists of 49 methods, totaling 4,373 retention times. External Datasets. A consequence of dividing the entire library of retention times into the training, validation, and testing splits is that it is very likely that in training, the model will "see" the embeddings of a method before the generalizability is tested. In other words, most methods will appear in the training sets before evaluation in the test set. From the machine learning perspective this causes no data leakage because models will never see the ground truth retention times. However, if we choose to understand learning RTs as a combination of the two tasks discussed in the Introduction section, this effect will likely result in some memorization/leakage with respect to task (ii), the chromatographic method rescaling. In this work, test the "true" generalizability of Graphormer-RT by using methods and retention times that the models have never seen. For experimental pre-screening, this measures how effectively Graphormer-RT can predict retention times for completely "new" methods and require no experimental measurements to accompany retention time prediction. We remove six (3 RP, 3 HILIC) "representative" methods from our dataset from all training workflows. Upon testing, we report the error of these methods separately to the testing dataset (which contains methods that could have been seen in training/validation) to provide a more complete measure of model generalizability. For RP systems, we choose methods 0029 (n=47), 0127 (n=93), and 0275 (n=75) as denoted in RepoRT. For HILIC datasets we use methods 0103 (n=70), 34 0283 (n=74), and 0375 (n=59). These methods provide a diverse but representative set of columns, manufacturers, and gradients to maximize the diversity of our testing protocols. Complete descriptions of these chromatographic methods are available in Figures .