A central challenge in biotechnology is to be able to predict functional properties of a protein from its sequence, and thus (i) discover new proteins with specific functionality and (ii) better understand the functional effect of genomic mutations. Experimental breakthroughs in our ability to read and write DNA allows data on the relationship between sequence and function to be rapidly acquired. This data can be used to train and validate machine learning models that predict protein function from sequence. However, the cost and latency of wet-lab experiments requires methods that find good sequences in few experimental rounds, where each round contains large batches of sequence designs. In this setting, model-based optimization allows us to take advantage of sample inefficient methods to find diverse optimal sequence candidates to be tested in the wet-lab. These requirements are illustrated by a collaboration that involves the design and experimental validation of AAV capsid protein variants that assemble integral capsids and package their genome, for use in gene therapy applications.