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While make-on-demand libraries now span trillions of molecules, full library docking struggles beyond a few billion, motivating prioritization strategies that recover top-scoring compounds while evaluating only a fraction of a library. Here we introduce a transparent prioritization approach, ChemSTEP (Chemical Space Traversal and Exploration Procedure), and define the effective size of a library treated by any prioritization algorithm, Neff. Grounded in ligand similarity, ChemSTEP docks a representative seed set, selects diverse high-scoring "beacons", and iteratively traverses the full library through cycles of beacon selection and similarity search. In retrospective calculations on eight targets and six billion molecules, ChemSTEP recovers over 75% of high-scoring compounds while docking less than 5% of the library. We then test ChemSTEP prospectively against AmpC β-lactamase using a 13.2 billion molecule library. Because AmpC recognizes anionic inhibitors, we explicitly docked all 360 million library anions, synthesizing and testing 241 high-ranking ones in parallel to the ChemSTEP 13.2B run; this serves as ground truth for the entire 13.2B enabling replicate ChemSTEP runs and direct comparison to brute-force campaigns. Compared with previous docking of 99 million and 1.7 billion molecules against AmpC, the 13.2 billion library had higher hit-rates (12% vs 41% vs 60%, respectively) and found 80% of the 241 high-ranking compounds within the first 0.5% docked. We estimate that trillion-molecule libraries might be prioritized within a month on a 5k-CPU cluster.