This paper summarizes the innovative solutions presented at the third edition of the SISAP Indexing Challenge held at SISAP 2025. The challenge featured two distinct tasks involving vector embeddings derived from a large corpus using neural encoders. It proposed the following two tasks under strict memory and computational constraints: Both tasks required solutions to operate within strict resource limits: 16 GB of RAM, 8 virtual CPUs, and a 12-h wall-clock time for the end-to-end pipeline (including data loading, pre-processing, indexing, and searching). Each task imposes different minimum quality requirements and ranking specifications. Participants developed strategies such as data compression, optimized indexing, and efficient search algorithms to meet these constraints. This paper details the challenge design, explains the evaluation framework, and provides an overview of the submitted solutions.