  This paper studies the $r$-range search problem for curves under the
continuous Fr\'echet distance: given a dataset $S$ of $n$ polygonal curves and
a threshold $r>0$, construct a data structure that, for any query curve $q$,
efficiently returns all entries in $S$ with distance at most $r$ from $q$. We
propose FRESH, an approximate and randomized approach for $r$-range search,
that leverages on a locality sensitive hashing scheme for detecting candidate
near neighbors of the query curve, and on a subsequent pruning step based on a
cascade of curve simplifications. We experimentally compare \fresh to exact and
deterministic solutions, and we show that high performance can be reached by
suitably relaxing precision and recall.
