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Abstract Virtual screening has long been a central computational tool for rational ligand discovery, enabling the systematic prioritization of candidate molecules from large chemical libraries. Although docking and related approaches that explicitly account for receptor–ligand interactions have been developed and refined over several decades, achieving both reliable receptor-aware interaction modeling and computational scalability remains an open challenge, particularly for ultra-large chemical spaces. Ligand-based methods are fast and robust but do not explicitly incorporate receptor structure, whereas docking-based approaches model receptor–ligand interactions more directly at substantially higher computational cost. Here, we present G-screen, a freely available and scalable receptor-aware virtual screening framework designed for cases in which a reference protein–ligand complex structure is available. Instead of performing full docking, G-screen rapidly aligns candidate ligands to the reference ligand using a flexible global alignment algorithm (G-align) and evaluates receptor-aware pharmacophore interactions derived from the reference complex, thereby combining the efficiency of ligand-based alignment with explicit atomic-level interaction analysis. Benchmarking on DUD-E, LIT-PCBA, and MUV datasets demonstrates that G-screen achieves competitive discrimination and early enrichment relative to representative ligand-based and docking-based methods, while maintaining millisecond-scale per-molecule runtimes under multi-threaded execution. These results position G-screen as a practical and scalable receptor-aware screening strategy for efficiently filtering large chemical libraries when a reference complex structure is available. Scientific Contribution We have developed a scalable virtual screening framework for efficiently filtering ultra-large chemical libraries using a flexible global alignment algorithm combined with receptor-aware pharmacophore evaluations. Despite explicitly capturing atomic-level interactions, the screening process using this method is highly efficient, maintaining millisecond-scale per-molecule runtimes under parallel execution. It achieves competitive discrimination and early enrichment, successfully bridging the speed of ligand-based approaches with the structural context of traditional docking.