Chemical reactions are stochastic in practice, resulting in yield variations even when reactions are repeated under identical conditions. Although HTE automation reduces this variability, some experimental noise is still expected. To assess the robustness of our optimisation workflow to such real-world variability, we simulated different noise levels by perturbing our model's input data with Gaussian noise of varying standard deviations (see Methods), comparing performance of the q-NEHVI acquisition function under these conditions. Our results showed that while there is some reduction in performance due to input noise, our optimisation approach remains robust even at high noise levels, such as a noise standard deviation of 10 (Fig. ). In practice, we expect HTE automation equipment to exhibit lower levels of noise, and our approach demonstrates reliable performance under these conditions. The ability of our workflow to handle appreciable levels of noise highlights its potential for real-world applications, where experimental variability is unavoidable. All noisy benchmark results can be found in Supplementary Information section 3.