  Data-driven design is making headway into a number of application areas,
including protein, small-molecule, and materials engineering. The design goal
is to construct an object with desired properties, such as a protein that binds
to a therapeutic target, or a superconducting material with a higher critical
temperature than previously observed. To that end, costly experimental
measurements are being replaced with calls to high-capacity regression models
trained on labeled data, which can be leveraged in an in silico search for
design candidates. However, the design goal necessitates moving into regions of
the design space beyond where such models were trained. Therefore, one can ask:
should the regression model be altered as the design algorithm explores the
design space, in the absence of new data? Herein, we answer this question in
the affirmative. In particular, we (i) formalize the data-driven design problem
as a non-zero-sum game, (ii) develop a principled strategy for retraining the
regression model as the design algorithm proceeds---what we refer to as
autofocusing, and (iii) demonstrate the promise of autofocusing empirically.
