This allows the construction of an objective function that maximises the average performance under variable conditions, while penalizing the expected variance of the results. This new surrogate model is highly correlated with the true robust objective, therefore, the predictions made by Golem exhibit increased accuracy and are useful in problems even with high levels of noise. This approach is designed to be agnostic to the experiment planning strategy or optimisation algorithm and is therefore useful in contexts beyond Bayesian optimisation. They benchmark their approach on synthetic optimisation tasks, including analytical chemistry protocols under noisy experimental conditions, finding considerable efficiency improvements in both systematic searches and those powered by Bayesian optimisation.