Odorant receptors (ORs) in Drosophila melanogaster are critical components of the insect olfactory system, mediating the detection of environmental cues such as host plants, food sources, and mating signals. Targeting these receptors with natural ligands offers a promising approach for sustainable pest control, especially through the utilization of bioactive compounds derived from agricultural crop and food production residues (ACFPR). In this study, we employed AlphaFold-predicted model of Drosophila odorant receptor Q9W1P8 (AF-Q9W1P8-F1-model_v4), retrieved from the UniProt database for structure-based virtual screening. Molecular docking was conducted using GNINA, a deep learning-enhanced docking platform that provides refined binding affinity predictions. The most favourable predicted binding affinities for 164 components of ACFPR from different sources were in the range -11.5 to -8.5 kcal/mol. Among the tested compounds, α-tomatine achieved the most favorable GNINA-affinity score (–11.5 kcal/mol). Other compounds, such as peonidin 3-rutinoside (–11.08 kcal/mol) and cinnamtannin B1 (–11.04 kcal/mol), also demonstrated strong predicted binding affinities according to GNINA scoring, supporting the potential of plant-derived molecules to modulate insect olfactory receptors. The binding modes of these molecules suggest potential interactions with multiple regions of the receptor surface, including the predicted binding site. These interactions may modulate receptor function through mechanisms such as direct inhibition of the binding site or allosteric regulation at distal sites. These findings highlight the relevance of odorant receptors as molecular targets for eco-friendly pest control, and demonstrate the utility of AlphaFold models and GNINA scoring in guiding the rational selection of natural ligands.