CAPRI challenges offer a range of blind tests for biomolecule interaction prediction. This study evaluates the performance of our prediction protocols for the human group Zou and the server group MDockPP in CAPRI rounds 47-55, highlighting the impact of AlphaFold2 (AF2) and the effectiveness of massive sampling approaches. Prior to AlphaFold2's release, our methods relied on homology modeling and docking-based protocols, achieving limited accuracy due to constraints in structural templates and inherent docking limitations. After AlphaFold2's public release, which demonstrated breakthrough accuracy in protein structure prediction, we integrated its multimer models and massive sampling techniques into our protocols. This integration significantly improved prediction accuracy, with human predictions increasing from 1 correct interface of 19 pre-AlphaFold2 to 4 of 8 post-AlphaFold2. The massive sampling approach further enhanced performance, particularly for targets T231 and T233, yielding medium-quality models that default parameters could not achieve.