Computational modelling has previously been used to provide a priori prediction and a posteriori rationalisation for experimental studies of POCs. High-throughput (HT) computational workflows can rapidly assemble precursor building blocks into cages of commonly observed topologies and assess both their shape-persistence (i.e., a target POC should have a permanent internal cavity which is not lost on the removal of solvent) and properties prior to synthesis. For example, Turcani et al. previously created the current largest computational database of >60,000 POC structures, with model assembly automated using the open-source Python package supramolecular toolkit (stk) and property calculation using the open-source Python package pyWindow, giving predictions on pore diameter, volume, and the number and size of cage windows. Automated assembly and prediction of cage properties can yield information on trends and the influence of precursor design on POC properties. However, the transition from computational prediction to experimental realisation is a common hurdle in the discovery of novel cages with desirable predicted properties. In an attempt to overcome this, Bennett et al. developed a computational HT workflow which classified 12,553 molecules as either 'easy' or 'difficult' to synthesise by expert chemists to then train a machine learning (ML) model (MPScore) to make this classification. This was incorporated into a computational HT screening workflow to screen for POCs formed from synthetically favourable precursor building blocks, and which possessed desirable predicted cage properties. Despite the ability to predict the properties of hypothetical POCs using an automated computational approach, difficulty still arises in predicting the type and/or topology of species that will be formed in a DCC reaction, as introduced in the challenges above. Due to the complexity of the dynamic system within a DCC reaction, a range of possible species may form. The species formed may be molecular in nature, such as the desired POC, or a kinetically trapped side product -these side products are frequently oligomeric or polymeric in nature and therefore precipitate out of the dynamic system, no longer benefitting from the error-correction mechanism. Even if a molecular cage species does form, Santolini et al. enumerated 20 probable topologies that POCs may form, further highlighting the difficulty in predicting and targeting a specific reaction outcome. In that work, density functional theory (DFT) was used to calculate bond formation energies for cages of different topologies to postrationalise the experimental formation of a mixture of topologies. Preferential formation of a single cage topology is likely attributed to it having the lowest formation energy, making it the most thermodynamically favoured, whereas, if a mixture of topologies is observed, it may be attributed to multiple cage topologies having a similar formation energy and thus no strong thermodynamic preference towards one topology is found. However, this approach does not guarantee successful cage formation, and does not consider experimental factors such as the solvent, overall concentration, reaction temperature or reaction time, all of which can affect the reaction outcome and lead to the formation of different species in DCC reactions. Data-led approaches combining experimental HTS and machine learning could rapidly accelerate this discovery process, but first, to do this, a HT workflow is needed for streamlined data collection and interpretation, ensuring a robust and reliable dataset is curated.