In this research, we utilized the Butina clustering algorithm and employed the ensemble learning method for both Voting and Stacking approaches. High probability values were incorporated to establish statistically significant pharmacophore models, as evidenced by improved performance measures such as AUCROC, EF1%, and GH score when compared to single top-scoring probabilistic models. Besides using the Feature selection, stacking model should be preferred due to reducing the training speed and computational resources for virtual screening. Utilization of an optimization approach for 3D pharmacophore models could be a potent strategy to successfully predict activity, efficiently design drugs, and screen large numbers of new compounds as active drug candidates.