To utilize the predicted RT predictions outside of a single laboratory/method framework for applications such as automated LC-MS metabolite annotation, a "method-independent" model for RT prediction is needed. We achieved this goal using a novel graph transformer with contextual encodings for chromatographic metadata that generalizes well across diverse methods, including those that the model has not seen in training. We also demonstrate how this framework can be extended to small molecule HILIC chromatography. Although this study presents a large step forward in the generalizable predictions of RTs, more work can be performed to further generalize and optimize model performance. While we have demonstrated that Graphormer-RT can generalize to a variety of chromatographic conditions, training data for RTs measured in other conditions (e.g., mobile phase of isopropanol, acetone) are needed for the model to meet the experimental chromatographic landscape. There are specific methods for which Graphormer-RT fails to generalize (e.g., complex gradients). Addressing these will require expanding the diversity of training data or expanding encoding formalisms. Experimental LC-MS has proven to be extremely powerful as a tool for separating, identifying, and annotating "dark" metabolites and other novel samples. With our contribution to "method-independent" predictions of RTs, we take a meaningful step towards the automated in silico annotation of unknown structures.