Generative molecular models produce candidate libraries containing structures with poor descriptor profiles or structural alerts that can be removed before docking or expert review. We test whether this early selection can be implemented as a reproducible filtering and ranking protocol. LiteGov applies medicinal-chemistry and structural-alert filters and ranks retained molecules using the quantitative estimate of drug-likeness (QED) and synthetic accessibility (SA), with fixed 0.8/0.2 weights. Across evaluations, mean QED increases from 0.665 to 0.887, mean SA decreases from 2.818 to 2.198, and pan-assay interference compound (PAINS) alerts decrease from 4.6% to 0%. In an exact ablation of 10,678 unique structures, LiteGov attains Spearman correlation 0.911, Top-100 overlap 0.620, and normalized discounted cumulative gain 0.977 against an author-specified property-priority reference; relative to QED-only ranking, Top-100 SA decreases by 0.232 with a QED difference of −0.003. Generator-held-out calibration selects weights of 0.64–0.68 and raises mean property-reference Spearman correlation from 0.900 to 0.917. AiZynthFinder route-solution rates increase from 0.58 to 1.00 for GenMol and from 0.64 to 0.80 for MoLeR. Complementary structure-aware audits report Spearman correlation 0.067 and Top-100 overlap 0.028 with an AutoDock Vina–GNINA affinity consensus, alongside a ChEMBL activity-label area under the receiver operating characteristic curve of 0.369. These evidence layers establish LiteGov as a descriptor-based triage and computational route-feasibility protocol for preparing candidate libraries for target-aware screening.
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