Being able to effectively design biological sequences like DNA and proteins would have transformative impact on medicine. Currently, the most popular method in the life sciences for performing design is directed evolution, which explores sequence space by making small mutations to existing sequences. Alternatively, Bayesian optimization (BO) provides an attractive framework for model-based black-box optimization, and has achieved many recent successes in life sciences applications. However, within the ML community, most large-scale BO efforts have focused on hyper-parameter tuning. These methods often do not translate to biological sequence design, where the search space is over a discrete alphabet, wet-lab experiments are run with considerable parallelism (1K-100K sequences per batch), and experiments are sufficiently slow and expensive that only few rounds of experiments are feasible. This paper discusses the particularities of batched BO on a large discrete space, and investigates the design choices that must be made in order to obtain robust, scalable, and experimentally successful models within this unique context.