Using gaseous CF3I for trifluoromethylation reaction, all substrates were converted to respective products in good yield (55-95%) in 8-16 mins (vs 12-72 h in batch). To be able to transfer any new photochemical transformation from the discovery stage batch protocols to continuous flow, it is desirable to assemble knowledge about the system in the form of models. Access to a good model may allow for an in silico design of flow reactions. However, as development of first principle mechanistic models of organic synthetic reactions is rather challenging and slow, recent focus in reaction development/optimisation is increasingly on using "self-optimising" experimental systems (automated experimental systems able to perform optimisation of a reaction, and, potentially, also separation, without human intervention), that are able to generate the required data and perform reaction optimisations with data-driven statistical methods, as opposed to the mechanistic models. When optimising for a single objective over continuous variables only, several optimisation algorithms such as Nelder-Mead-Simplex, SNOBFIT, and steepest descent have been implemented for different types of reactions, such as nanoparticle synthesis, heterogenous catalytic reactions ). With no prior model of the chemical reactions, these "black box" approaches focus on relationships between input and output variables or objective(s). These algorithms have some significant limitations: they are either local search algorithms with limited scalability to larger systems (e.g., Nelder-Mead-Simplex), require expensive derivative estimations and are inaccurate for noisy systems (e.g., steepest descent), or have slow convergence despite successful implementations as a global search algorithm (SNOBFIT). Moreover, most of the published works on self-optimisation are limited to optimising up to five continuous variables for a single objective. The more advanced versions of self-optimisation methods are based on Bayesian optimisation (BO) algorithms, such as MOAL, TS-EMO, MVMOO, and Google Vizier, which construct non-parametric statistical models, so-called Gaussian processes, using a sequential active learning approach (i.e., re-training a model when new experimental observations become available). BO approaches utilise all available data to build statistical models and are thus data efficient. The models provide quantification of uncertainty that is used to solve the inherent exploration-exploitation trade-off within derivative-free optimisation. Extensions of TS-EMO algorithms have been successfully implemented for multi-objective self-optimisation and solvent selection. For a holistic and scalable process development, an ideal algorithm should optimise for multiple competing objectives, account for the inherent explorationexploitation trade-off by iteratively minimising prediction uncertainty, and optimise for continuous and discrete variables simultaneously.