  The ability to design and optimize biological sequences with specific
functionalities would unlock enormous value in technology and healthcare. In
recent years, machine learning-guided sequence design has progressed this goal
significantly, though validating designed sequences in the lab or clinic takes
many months and substantial labor. It is therefore valuable to assess the
likelihood that a designed set contains sequences of the desired quality (which
often lies outside the label distribution in our training data) before
committing resources to an experiment. Forecasting, a prominent concept in many
domains where feedback can be delayed (e.g. elections), has not been used or
studied in the context of sequence design. Here we propose a method to guide
decision-making that forecasts the performance of high-throughput libraries
(e.g. containing $10^5$ unique variants) based on estimates provided by models,
providing a posterior for the distribution of labels in the library. We show
that our method outperforms baselines that naively use model scores to estimate
library performance, which are the only tool available today for this purpose.
