Liquid chromatography (LC) has long been a cornerstone of analytical separations, providing a reliable means to isolate metabolites, peptides, pharmaceuticals, and xenobiotics. The retention time (RT) is the defining parameter of this technique, describing the amount of time required for a particular analyte to be carried by a solvent mixture (i.e., a mobile phase) through a column packed with a stationary phase. LC is commonly coupled to mass spectrometry (MS) to separate and characterize molecules in complex mixtures such as those analyzed in "-omics" studies (i.e., proteomics, metabolomics, lipidomics, etc.). In targeted MS experiments, predicted RTs have been used to differentiate isobaric lipids, reduce false-positive annotation rate of small molecule MS 2 fragmentation spectra, and increase the accuracy of peptide identification schema. In general, LC-MS annotation using machine learning has been shown to meaningfully increases the confidence of small molecule, metabolite, peptide, and cell/organelle interactome identities. Quantitative structure-property relationships (QSPRs) have been used for decades to derive relationships with and predict analyte RTs. However, despite the many machine learning (ML) models that have been reported to date, there exists no generalizable framework for the method-independent prediction of RT. Comparison of chromatographic methods between systems is difficult because of variances in measured RTs due to differences in parameters such as column properties, mobile phase composition, gradient profile, flow rate, pH, temperatures, and matrix effects to name a few. Current, successful, deep learning frameworks utilize an internally consistent set of predictions for a singular chromatographic setup. When the method is held constant, this multi-dimensional description of column and gradient is learned implicitly by the model because these parameters are invariant to all RTs. This paradigm is embodied by the METLIN SMRT dataset, a library of more than 80,000 small molecule RTs obtained using a single LC method. Employing this database, RTs have been predicted to within less than a minute using artificial neural networks (ANNs), ensemble regressors, Graph Neural Networks (GNNs), and Convolutional Neural Networks (CNNs). Prediction of RTs should be thought of as two complementary tasks: (i) learning the relationship between molecular structure and degree of interaction with the mobile and stationary phases and (ii) scaling those interactions to a given set of chromatographic conditions. Because the knowledge gained from (i) is consistent across chromatographic setups, transfer learning has been used to pass this information to setups where (ii) is learned upon finetuning. This approach has been successfully applied for RTs in smaller datasets where the chromatographic conditions are internally consistent and the "method rescaling" is learned implicitly. However, in these cases, the prediction error can be as much as double the first dataset (ca. 1-2 minutes) and questions about generalizability remain unanswered. Although progress in RT prediction is encouraging, there yet exists no method-independent RT prediction tool. To create an accurate method-independent model, one needs a sufficiently large RT prediction dataset that contains the necessary standardized method data (e.g., intrinsic column data, gradient, etc.) and a model that can combine tasks (i) and (ii) in context of each other to produce sufficiently descriptive embeddings. In 2024, the RepoRT dataset was published, providing the necessary chromatographic library and metadata needed to describe the total set of column conditions.