In conclusion, we have established a practical framework for analyzing conformational searching output and generating priority lists for re-optimizing conformers at the DFT level. The automated clustering approach pipeline extracts dihedral angle values as descriptors from conformers, performs the agglomerative clustering process and generates the priority list. Thorough evaluations were conducted through the global minimum test and the bins test based on a large dataset of 150 structurally diverse molecules. The priority list from the pipeline intuitively guides the DFT re-optimization process and reduces the chance of getting duplicate conformers by up to 51% for the first 30% of the calculations. Machine learning models for predicting the completion of the re-optimization process were trained and evaluated. Users can finish their DFT re-optimization process at an earlier stage with the confidence that the global minimum has been obtained based on the p LR value. This project led to the development of the CONFPASS program, which covers the clustering approach pipeline for generating priority lists and tests for predicting the completion of the re-optimization process. CONFPASS is fully automated and offers rapid analyses. We have tested CONFPASS with FF structures from MacroModel and semi-empirical structures from CREST, which demonstrates the robustness of the program. In the future, the CONFPASS program will be further developed to cope with organometallics and multimeric systems. Adapting the program for finding transition states is also an interesting aspect to investigate.