Abstract: One natural framework for structured design problems is black-box optimization. For example, a biochemist might aim to design some object of interest (e.g. a peptide or molecule) which optimizes some objective function (i.e. its inhibitory activity against some target pathogenic bacteria). Typically, no mathematical form is available for this objective function, and it may be expensive to evaluate, making it a black box. Bayesian optimization (BO) is a popular machine learning method for solving black-box optimization problems. However, typical BO methods assume a continuous numerical search space, thus preventing them from being applied to search over structured, discrete objects such as peptides. A promising solution to this issue is a newly emerging technology for this setting, generative Bayesian optimization. Generative BO utilizes a generative model to map the structured search space to a continuous latent space. This enables the use of standard BO techniques to perform the search for new objects in the continuous latent feature space, rather than directly over the discrete space of structured objects. In this talk, I will discuss Generative BO in the context of designing complex structured objects, with a focus on our recent work to improve current Generative BO methods, and extend these methods to be better suited to the particular needs of practitioners.