This paper presents a practical implementation of a fast and memory-efficient approximate nearest neighbor (ANN) search system based on a two-stage filtering strategy using short and long quantized projections. The proposed system builds on a method recently developed by our group and is tailored for Task 1 of the SISAP 2025 Indexing Challenge, utilizing publicly available datasets and evaluation protocols. The indexing method, referred to as double filtering, uses two types of quantized dimension-reduction projections: short binary sketches derived by 1-bit quantization, and longer projections for refined filtering. Both are obtained by applying dimension reduction to the original high-dimensional vectors and quantizing the results. To meet the challenge’s hardware constraints (16 GB RAM and 8 CPU cores), we tuned parameters such as sketch width and projection dimensionality. Unlike systems optimized for batch processing and throughput, our method operates in an online setting, where each query is processed sequentially and independently. We focus on per-query response latency, measuring average time and variability, which makes the system well-suited for real-time or interactive applications under resource limitations.