  Domain experts often need to extract structured information from large
corpora. We advocate for a search paradigm called ``extractive search'', in
which a search query is enriched with capture-slots, to allow for such rapid
extraction. Such an extractive search system can be built around syntactic
structures, resulting in high-precision, low-recall results. We show how the
recall can be improved using neural retrieval and alignment. The goals of this
paper are to concisely introduce the extractive-search paradigm; and to
demonstrate a prototype neural retrieval system for extractive search and its
benefits and potential. Our prototype is available at
\url{https://spike.neural-sim.apps.allenai.org/} and a video demonstration is
available at \url{https://vimeo.com/559586687}.
