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The pharmacologically characterized receptor subtype of the serotonin family, the 5HT1A receptor is implicated in the pathophysiology and treatment of depression and anxiety-related disorders. Being the most extensively targeted receptor for developing novel antidepressants and anxiolytics, a near-ideal theoretical model can aid in high-throughput screening of promising drug candidates. However, the design of potential drug candidates suffers owing to a lack of complete structural information. In this work, homology models of 5-HT1A receptor are generated using two distinct alignments (CW and PSTA) and model building methods (KB and EB). The developed models are validated for virtual screening using a ligand dataset of agonists and antagonists. The best-suited model was efficient in discriminating agonist/antagonist binding. Correlation plots between pKi and docking (R²_agonist≥ 0.6, R²_antagonist≥ 0.7) and MM-GBSA dG bind values (R²_agonist≥ 0.5, R²_antagonist≥ 0.7) revealed optimum corroboration between in vitro and in silico outcomes, which further suggested the usefulness of the developed model for the design of high-affinity probes for the neurological disorders.