Bayesian experimental design (BED) provides a powerful and principled information-theoretic framework for optimizing the design of experiments. However, its deployment often poses substantial computational challenges that can undermine its practical use. In this talk, we will outline how recent advances have transformed our ability to overcome these challenges and thus utilize BED effectively. We will discuss techniques aimed at making BED more practical for real-world applications and highlight open source tools for their implementation.