Secreted proteins are extracellular ligands that play key roles in paracrine and endocrine signaling, classically by binding cell surface receptors. Experimental assays to identify new extracellular ligand-receptor interactions are challenging, which has hampered the rate of novel ligand discovery. Here, using AlphaFold-multimer, we developed and applied an approach for extracellular ligand-binding prediction to a structural library of 1,108 single-pass transmembrane receptors. We demonstrate high discriminatory power and a success rate of close to 90 % for known ligand-receptor pairs where no a priori structural information is required. Importantly, the prediction was performed on de novo ligand-receptor pairs not used for AlphaFold training and validated against experimental structures. These results demonstrate proof-of-concept of a rapid and accurate computational resource to predict high-confidence cell-surface receptors for a diverse set of ligands by structural binding prediction, with potentially wide applicability for the understanding of cell-cell communication.