The first step in the funneling procedure is the generation of a large number of configuration candidates. The key idea is sampling a large part of the potential energy surfaces at a low level of theory to get estimates for the global free energy minimum. This is usually carried out using force-field methods in combination with genetic algorithms such as in AB-Cluster 9,10,12-15 and OGOLEM, by random/manual sampling or using dynamic methods such as basin hopping. The major issue at this step is that most force-field methods are unable to describe bond-breaking, such as proton transfer reactions, which are important for atmospherically relevant molecular clusters, requiring the sampling to include monomers where the hydrogens have been transferred to get adequate sampling. Furthermore, the accuracy of force-field methods is also insufficient to determine a subsample of the conformer candidates and all the candidates have to be taken to the higher level of theory. The next step is semi-empirical calculations as these are a better description of the chemistry and filtering can be applied. Of the common semiempirical methods, GFN1-xTB, 27 GFN2-xTB, 28 PM6, and PM7 30 are the most used in configurational sampling procedures for atmospheric molecular clusters. Of these methods, GFN1-xTB has shown to have the highest correlation with electronic binding energies at a higher level of theories and have been shown to have a higher correlation with DFT trajectories for molecular dynamics than GFN2-xTB. The reason GFN2-xTB performs worse than GFN1-xTB for atmospheric molecular clusters (often involving sulfuric acid) is that there is a decrease in the number of d -functions for sulfur in the basis set for the newer GFN2-xTB model. The third step is the subsequent optimization and vibrational frequency calculation of the structures with DFT. This is the main bottleneck in the sampling methodology as limited computational resources only allow a fixed number of DFT structures to be optimized. Therefore some form of filtering is required, often based on structural properties or electronic energies from the semiempirical calculations. To circumvent the inaccuracies of semiempirical methods an intermediate step can be included, involving single-point energy calculations at the DFT level on as many structures as possible. Another option for an intermediate step is the utilization of machine learning (ML) methods. One can calculate a subset of the structures at a desired DFT level and train an ML model to predict the energies of the remaining structures. However, to mimic accurate DFT energies, kernel-based ML methods become computationally demanding and neural-network will require an extensive set of training data hyperparameter optimization. Moreover, ML methods often fail when predicting on structures different from the training set.