With approximately 400 encoding genes in humans, odorant receptors (ORs) are the largest subfamily of class A G protein-coupled receptors (GPCRs). Despite its high relevance and representation, the odorant-GPCRome is structurally poorly characterized: no experimental structures are available and the low sequence identity of ORs to experimentally solved GPCRs is a major challenge for their modeling. Moreover, the receptive range of most ORs is unknown. The odorant receptor OR5K1 was recently and comprehensively characterized in terms of cognate agonists. Here we investigate the binding modes of identified ligands into the OR5K1 orthosteric binding site using structural information both from AI-driven modeling, as recently released in the AlphaFold Protein Structure Database, and from template-based modeling. Induced-fit docking simulations were used to sample the binding site conformational space for ensemble docking. Side chain residue sampling and model selection were guided by mutagenesis data. We obtained models that could better rationalize the different activity of active (agonist) versus inactive molecules with respect to starting models, and also capture differences in activity related to small structural differences. We, therefore, provide a model refinement protocol that can be applied to model the orthosteric binding site of ORs as well as that of GPCRs with low sequence identity to available templates.