The lack of experimental structures for the majority of protein–protein complexes has motivated the development of a variety of strategies for the structural modeling of protein complexes, such as computational docking, in active development for the last decades, and the more recent artificial intelligence (AI)-based ground-breaking methodologies. Among the existing computational docking methods, Python docking (pyDock) has shown competitive predictive rates and high robustness over the years. However, the field has dramatically changed with the appearance of artificial intelligence (AI)-based methods, like AlphaFold. While structure prediction of individual proteins is virtually solved by this program, the focus is now on how to improve the prediction of challenging cases like antibody–antigen complexes, multiprotein complexes, weak interactions, or highly flexible interacting proteins. Successful strategies are based on the generation of more diverse sets of models and the integration with other “classical” approaches that facilitate the identification of the correct models. Here, we will show in practical terms how to combine the structural modeling capabilities of AlphaFold with the energy-based scoring function in pyDock to improve structural predictions in challenging protein–protein complexes.