Approximate nearest neighbour (ANN) search is central to modern data-intensive applications, particularly on large-scale, heterogeneous, or high-dimensional datasets. However, many existing ANN methods struggle in such scenarios, either because they rely on metric assumptions or because their indexing strategies are ill-suited to distributed environments or memory-constrained settings. This talk introduces PDASC (Parametrizable Distributed Approximate Similarity Search with Clustering), a distributed ANN search algorithm whose index design addresses three common concerns in ANN search: supporting arbitrary dissimilarity functions, native distributed execution, and efficient operation under constrained memory environments.