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is a common Gram-negative pathogen with increasing resistance to all recommended antibiotics. There is a critical need to improve the efficiency of the antibiotic hit discovery process to replenish the drug development pipeline. Here, we show that deep learning models can augment high-throughput screens to identify readily available molecules with narrow-spectrum activity against difficult-to-treat strains of . We phenotypically tested 38,650 small molecules for growth inhibition to train a predictive graph neural network (GNN) model. We benchmarked the model's performance against other architectures, including a large language model, and found that GNNs more accurately identify active, drug-like molecules that are structurally distinct from the training set and known antibiotics. Using the model to virtually screen ~6 million compounds, we identified 213 compounds for experimental validation and found that 83 (39%) inhibited growth. Two of these compounds were structurally dissimilar to existing antibiotics, maintained potency against multidrug-resistant strains in vitro, exhibited promising selectivity indices, and were rapidly bactericidal with low frequencies of resistance. Proteomic studies revealed their distinct mechanisms of action, with one compound targeting alanine racemase, an enzyme involved in the essential process of peptidoglycan synthesis. Furthermore, the compounds showed early promise in reducing titers in a human vagina-on-a-chip infection model and a mouse vaginal infection model. Our work establishes the deep learning-enabled discovery of selective antibacterial compounds against as a much-needed hit discovery tool to address the growing crisis of antimicrobial resistance for this pathogen.