During LumiNet training, we also do not update the pre-trained model parameters, instead opting to fine-tune solely the physical scoring component while retaining the original structural parameters. This strategy ensures rapid adaptation to new systems. As shown in Table , we selected several of the best-performing and most classical models reported for this dataset as baselines, including four DL-based models and four classical method-based models. Among them, the 2D3D hybrid model 31 combines AttentiveFP 36 and RF-PLP , while the "extend data" approach signifies the model's utilization of both docked poses and supplementary training with molecules possessing only 2D structures. Moreover, we included the models developed by Isert et al. in 2024, who predicted ABFE using electron density-based geometric DL and tested it on PDE10A , as part of our baselines.