First, the turbidity of the reactions was screened using computer vision software adapted from the open-source Python package Heinsight 20 which, broadly, uses the difference in brightness between the region of interest (ROI) of a reference and the measured sample in recorded images to determine whether a solution is turbid. Using the dissolved reference ROI measurement, a turbidity threshold was determined. If the sample's measured turbidity was below this threshold, it was characterized as 'dissolved' and therefore assumed to be a molecular species. Whereas if the measured sample's turbidity was above this threshold, it was characterized as 'not dissolved' and as such, it was assumed that an insoluble oligomeric, polymeric, or COF side-product had been formed. Each of the latter outcomes is generally undesirable for POC synthesis, removing species from equilibrium, reducing the formation and subsequent yield of any associated POC, and necessitate, at the very least, an additional filtration step to isolate the desired soluble cage species. Further extraction and processing of the data was conducted using an in-house Python script that categorised whether a measured sample was 'dissolved' or 'not dissolved' and if it matched researcher assessment. Typically, cases where the computer vision outcome did not match researcher observation were where small, clear crystals had formed on the side of the vials, above the selected ROI, which may alternatively be attributed to POC crystallisation. However, computer vision successfully categorised 96% of the reaction outcomes when compared to researcher assessment (Figure ) and we deemed this level of accuracy to be sufficient for taking the method forward.