CONFPASS is designed for obtaining all low-energy structures at a higher level of theory with a confidence level that all relevant structures have been found. CREST is often used for conformational studies and has a different aim: to generate representative structures, which is very useful for predicting spectroscopically relevant structures and finding macroscopic properties. We performed conformational searching calculations using CREST and re-optimized all the conformers from the output files at the DFT level for a random selection of 20 molecules from the master dataset (ie the CREST-DFT dataset). 11 of the 20 CREST-DFT dataset molecules overlap with the DFT dataset, which allows us to compare the performance between CREST and MacroModel as the starting point of DFT re-optimization processes. We compared DFT structures re-optimized from the CREST and MacroModel output for the 11 molecules in terms of the Boltzmann distribution population. In most cases, the same global minimum and low energy structures were identified upon DFT re-optimizations from the CREST and Macro-Model output. However, conformational searches with Macro-Model were more than 8 times faster compared to CREST. Although more conformer structures were found with CREST compared to MacroModel on average, 53% of the DFT re-optimized structures in the CREST-DFT dataset were duplicate structures. This means that CREST is much slower overall because it has to do many more DFT optimizations to get to the same outcome. Re-optimizing all conformers from CREST searches needs 1.6 times more CPU time than re-optimizing all the conformers from MacroModel. Using CONFPASS to give a greater than 90% confidence, with FF structures from Macro-Model as the starting point, is more than 3.8 times faster than re-optimizing all conformers from CREST outputs. (SI Section 8.D.) CONFPASS is flexible and able to accept conformational searching output files from any program that can generate SDF. Thus, CONFPASS can also accept output files from CREST. With the CREST-DFT dataset, the global minimum and bins test were performed and overall parameters were calculated on various priority list generation methods. Similar results were obtained compared to the result from the DFT dataset. We then ran tests with the LR model for predicting the completion of the re-optimization process with the CREST-DFT dataset. On average, CONFPASS gives more than 90% confidence after re-optimizing 42% of the conformers following the default priority list, which covers 90% of the Boltzmann distribution population. (SI Section 8.C.) The above investigation demonstrates the robustness of CONFPASS in accepting output files from calculations at different levels of theory and conformational searching methods. The prioritization and pas test prediction from CONFPASS can help obtain the global minimum and key lowenergy structures with a reduced computational cost. If the aim is to find representative DFT geometries, CREST is the appropriate choice. If you need the lowest energy DFT structures and a confidence level that you have found them, then CONFPASS gives this information faster and more effectively.