Training or fine-tuning models with up-to-date information is another powerful approach but comes with drawbacks. Fine-tuning requires significant advance work to prepare a model tailored to a specific need. In contrast, approaches that can be performed at inference time offer the advantage of being applicable to any model without retraining the weights, thereby preserving generalizability. This combination could be especially useful in drug discovery, where accurate molecular property predictions are crucial for assessing drug permeability and potential efficacy early in the development pipeline.