To understand the reactivity of a structurally complex and flexible molecule, computational chemists often start with conformational searching calculations using a fast method, such as a force field (FF). The FF structures are re-optimized using density functional theory (DFT) which is more accurate but comparatively slow. Further analyses, such as transition state (TS) optimizations and non-covalent interactions analyses, can then be conducted to rationalize the reactivity and selectivity. DFT optimizations of the FF level structures are a bottleneck in this process. Conformer generators, such as MacroModel, OMEGA and RDKit 18 , can analyze the results of conformational searches and remove duplicate structures and high-energy conformers. The search for even better approaches is ongoing. CREST (Conformer-Rotamer Ensemble Sampling Tool) is a state-of-art program which performs conformational searching calculations with the iterative meta-dynamics with genetic crossing (iMTD-GC), based on a semi-empirical method (GFN2-xTB). At the end of the process, conformers are identified from rotamer ensembles based on RMSD, energy and rotational constant thresholds. Even using such procedures, the conformational searching output files of structurally flexible molecules may include hundreds of relevant structures. Optimizing all the conformers from the output files at the DFT level requires a huge amount of computational time. Sometimes, distinct FF structures may end up having the same conformation at the DFT level. However, re-optimizing just a few conformers with low FF energy is also insufficient. Thorough explorations of the conformational space are critical for understanding reactivities. The correlations between the FF or semi-empirical potential energy surface (PES) and the DFT PES are difficult to predict for individual molecules, which has been reported in benchmarking studies. As is well known, a relatively high energy conformer (e.g. ΔU(FF) = 4 kcal mol -1 ) at the FF level might be the global minimum at the DFT level. To take a simple example: a molecule with ten FF conformations. If the lower energy five are re-optimized using DFT, is it necessary to re-optimize the other conformations? In this paper, we present CONFPASS (Conformer Prioritizations & Analysis for DFT re-optimizations) a program which provides an answer to this question. CONFPASS starts with the output from a FF-based conformational searching program, with the conformers sorted in order of increasing FF energy. If these are all reoptimized at DFT level (Figure ) to generate a new list, ordered by DFT energy, the new list may well contain fewer structures, as different FF structures may optimize to the same DFT structure. Whilst structures with high FF energies may re-optimize to structure with energies closer to the DFT global minimum, structures with very high FF energies may be safely discarded. Several strategies can be used for deciding on the order for DFT re-optimization. A simple but time-consuming approach is to re-optimize the structures according to the energy at the FF level from the lowest to the highest. We call this the ascent approach. This can be made faster by just selecting every n th conformer from the FF output file, the every n th approach. When n = 1, this is the same as the ascent approach. We can also randomly pick a sample of conformers for re-optimizations, the random approach. For large and flexible systems, the last two approaches risk missing out key parts of the conformational space. Several studies have proposed methods to balance speed with confidence in the outcome. For example, Johannessen et al. process conformational sampling outputs with principal component analysis and clustering algorithm to assist Raman optical activity spectral analyses of vancomycin, a glycopeptide. Merz Jr. et al. have developed AutoGraph, which clusters conformers and produces conformational graphs for metabolites, such as oxidized nicotinamide adenine dinucleotide. We previously built an automated pipeline to select representative conformers for DFT re-optimizations using a conformation labelling system based on dihedral angle values specifically aimed at studying the reactivity of diarylprolinol silyl ether catalysts. CONFPASS builds on our earlier approach to generate a general pipeline for diverse organic structures. Our new procedure, CONFPASS, chooses the optimal order in which to reoptimize the FF structures based on a clustering approach and then assesses how much confidence can be had in incomplete calculations as the re-optimization progress. Thorough evaluations based on a dataset of structurally diverse molecules have shown that CONFPASS is effective in assisting organic chemists explore conformational space and structures.