For some years now a trend toward the employment of high-throughput technologies in the development of new catalysts has been apparent in academic and industrial research. One reason is the unmanageably large parameter space present in heterogeneous catalysis, which implies that an increase in throughput in the preparation and testing of candidate substances is necessary, in order to be able to identify a new catalyst in a practicable amount of time. We have tried to locate the Current boundaries in achievable throughput. To this end, we developed a workflow in which 10,000 substances per day were synthesised and their activity tested in a heterogeneously catalysed gas phase reaction (alkene epoxidation). The substances were synthesised by dosing precursor solutions onto a single substrate, using 1 ink-jet printer technology and subsequent thermal treatment. I For the activity testing, the product stream of each candidate was conducted through a detection layer, where the target product was converted into a fluorescent substance. By locally resolved fluorescence spectroscopy a rough assessment of each candidate was possible. Because a throughput of 10,000 substances per day is still not able to map the whole parameter space in a practicable amount of time, we used a combination of evolutionary optimisation and data mining as our experiment design strategy for the reduction of the experimental parameter space. It became apparent that a throughput of 10,000 substances per day was only possible at the cost of abstraction and simplification with a concomitant reduction in knowledge gain per individual experiment. Because not every reaction is suited to be simplified in such a manner, whether a high-throughput screening approach is effective or not has to be considered on a case by case basis.