These considerations in mind, there are a number of benefits we see with the threshold clustering approach that warrant further development of the method. Foremost is the compatibility with a large range of energy models, especially those common in CSP, allowing direct comparison between the reduced structure set and the original predicted landscape. Despite good arguments for including dynamics in CSP, static methods predominate and will likely remain a large part of CSP for the foreseeable future. Hence, a method for reducing overprediction that is compatible with these methods is worthwhile. A further and perhaps less obvious benefit is the simplicity of the method. Indeed, the primary obstruction to wider adoption of methods for reducing overprediction is the added complexity and cost over performing the initial CSP, the general opinion tending towards investing resources in more accurate energy rankings as opposed to reducing candidate structures. However, the cost of threshold clustering is reasonable, the bulk being in the lattice energy minimisations as opposed to the Monte Carlo trajectories, which can be relatively short due the small, highly constrained search space. This is further emphasised considering the convergence of our simulations (Fig. ), which suggests we could have achieved almost identical results with a third or less of the computational cost. Despite erring on the side of over-sampling, the benzene and acrylic acid results were still produced in a day, and the resorcinol results, using DFTB, in less than three days. We expect this small cost to make threshold clustering practical and appealing to CSP researchers and practitioners, and moreover, from our own experience, the fact that it uses many tools common to conventional CSP methods lends threshold clustering to being highly accessible.