Scoring functions evaluate each pose based on its potential energy, interaction with the protein, and other physicochemical properties. The poses are then ranked based on their scores, with lower scores generally indicating more favorable interactions (higher binding affinity). The pose with the best (lowest) score is typically considered the most likely binding conformation. However, selecting the top-scoring pose as the best solution is not always reliable. It is essential to consider the distribution of scores and the differences between the top poses. In some cases, additional criteria such as the presence of key interactions (e.g., hydrogen bonds with crucial residues) or the fit within the binding pocket are also considered to validate the selected pose. Scoring functions are not perfect and often face challenges such as accurately capturing solvent effects, protein flexibility, and entropic changes. Due to these limitations, the top-scoring pose might not always represent the experimentally observed binding mode. Therefore, it is recommended to analyze multiple top-ranking poses and consider additional validation methods such as molecular dynamics simulations or experimental data. To improve the reliability of pose selection, consensus scoring can be used. This approach combines scores from multiple scoring functions to balance out their individual biases and errors, potentially leading to a more accurate selection of the best pose .