  Designers rely on visual search to explore and develop ideas in early design
stages. However, designers can struggle to identify suitable text queries to
initiate a search or to discover images for similarity-based search that can
adequately express their intent. We propose GenQuery, a novel system that
integrates generative models into the visual search process. GenQuery can
automatically elaborate on users' queries and surface concrete search
directions when users only have abstract ideas. To support precise expression
of search intents, the system enables users to generatively modify images and
use these in similarity-based search. In a comparative user study (N=16),
designers felt that they could more accurately express their intents and find
more satisfactory outcomes with GenQuery compared to a tool without generative
features. Furthermore, the unpredictability of generations allowed participants
to uncover more diverse outcomes. By supporting both convergence and
divergence, GenQuery led to a more creative experience.
