Our HTE platform utilises four heating wells for temperature control, restricting a single batch of experiments to a maximum of 4 unique reaction temperatures. To maintain practicality and minimise plate splitting across different heating wells, we restricted optimisation campaigns to 2 unique temperatures per batch. Such temperature constraints are also commonly observed in non-automated laboratory settings, for example, the finite number of heating plates in a fume hood. To implement this constraint into batch selection with our acquisition functions, we extended the naive and nested approaches from Vellanki et al. to accommodate multiple unique allowed temperatures per batch and multi-objective acquisition functions. The naive approach uses standard Bayesian optimisation to select batch experiments until the number of unique temperatures meets the constraint (e.g., 2). The remaining experiments are then restricted to being selected from only those 2 unique temperatures. The nested approach first algorithmically selects the most promising temperatures (see Methods), then identifies optimal reaction conditions within constraints of the selected temperatures. Both approaches can be extended to an arbitrary number of constrained temperatures per batch.