NextTopDocker, a largest-scale, up-to-date (as of May 2025), and fully open-access data set of 19,239 PDB-derived protein-ligand complexes, split into 14,038 training and 5,201 test entries via a strict cold-ligand strategy, together with nine ligand-similarity-aware training subsets, provides a challenging, diverse, and reproducible foundation for evaluating pose generation and docking performance. On this benchmark dataset, our simple logistic regression models, LogReg (x%), trained on Smina and GNINA 1.3 scores from chemically dissimilar ligands and applied to Smina-generated poses, achieved docking power comparable to or exceeding that of the four SOTA end-to-end ML docking tools (DeepDock, Interformer, SurfDock, and Uni-Mol Docking v.2).