CCR2 antagonists that disrupt CCR2/MCP‐1 interaction are expected to treat a variety of inflammatory and autoimmune diseases. The lack of CCR2 crystal structure limits the application of structure‐based drug design (SBDD) to this target. Although a few three‐dimensional theoretical models have been reported, their accuracy remains to be improved in terms of templates and modeling approaches. In this study, we developed a unique ligand‐steered strategy for CCR2 homology modeling. It starts with an initial model based on the X‐ray structure of the closest homolog so far, i.e. CXCR4. Then, it uses Elastic Network Normal Mode Analysis (EN‐NMA) and flexible docking (FD) by AutoDock Vina software to generate ligand‐induced‐fit models. It selects optimal model(s) as well as scoring function(s) via extensive evaluation of model performance based on a unique benchmarking set constructed by our in‐house tool, i.e. MUBD‐DecoyMaker. The model of 81_04 presents the optimal enrichment when combined with the scoring function of PMF04, and the proposed binding mode between CCR2 and Teijin lead by this model complies with the reported mutagenesis data. To highlight the advantage of our strategy, we compared it with the only reported ligand‐steered strategy for CCR2 homology modeling, i.e. Discovery Studio (DS)/Ligand Minimization. Lastly, we performed prospective virtual screening based on 81_04 and CCR2 antagonist bioassay. The identification of two hit compounds, i.e. E859‐1281 and MolPort‐007‐767‐945 validated the efficacy of our model and the ligand‐steered strategy.