We propose a competition to encourage the development of indexing data structures and search algorithms for the Approximate Nearest Neighbor (ANN) or Vector search problem in real-world scenarios. Rather than evaluating the classical uniform indexing of dense vectors, this competition proposes to focus on difficult variants of the task. Optimizing these variants is increasingly relevant as vector search becomes commonplace and the "simple" case is sufficiently well addressed. Specifically, we propose the sparse, filtered, out-of-distribution and streaming variants of ANNS.These variants require adapted search algorithms and strategies with different tradeoffs. This competition aims at being accessible to participants with modest compute resources by limiting the scale of the datasets, normalizing on limited evaluation hardware, and accepting open-source submissions to only a subset of the datasets.This competition will build on the evaluation framework https://github.com/harsha-simhadri/big-ann-benchmarksthat we set up for the billion-scale ANNS challenge https://big-ann-benchmarks.com of NeurIPS 2021.