By empowering domain experts to perform interactive exploration of large time series datasets, sketch-based query interfaces have revitalized interest in the well-studied problem of time series similarity search. In this new interaction paradigm, recent similarity algorithms (e.g., Qetch, Peax, LineNet) that attempt to capture perceptually relevant features have supplanted older, more straightforward distance measures (e.g., Euclidean, DTW). However, the downside of these algorithms is the resulting difficulty in designing corresponding index structures to support efficient similarity search over large datasets, thus necessitating brute-force search. This demo will showcase Deep Time Series Similarity Search (DTS3), our pluggable indexing pipeline for arbitrary distance measures. DTS3 can automatically train a foundation model for any custom, user-supplied distance measure with no strict constraints (e.g., differentiability), thus enabling fast retrieval via an off-the-shelf vector DBMS. Using our DeepSketch web interface, participants can compare DTS3 to the baseline brute-force versions of several similarity algorithms to see that our approach can achieve much lower latency without sacrificing accuracy when searching over large, real-world time series datasets.