Time series snippet discovery aims to summarize complex sequences by extracting representative subsequences that capture predominant behaviors. While Matrix Profile-based methods like Snippet-Finder offer strong robustness, they face significant computational challenges with long-duration or high-resolution time series, often requiring \( O(M^2) \) operations. We propose RS4 (Restricted Search Space for Snippet Selection), a hybrid method that integrates clustering techniques with a refined Matrix Profile Distance search strategy. Our approach first segments and normalizes subsequences, applies clustering to identify cohesive groups, selects medoids as candidate snippets, and computes a restricted MPdist profile for final selection. This approach reduces the search space complexity to \( O(Mn) \) , where \( n \) is the number of clusters, without sacrificing pattern fidelity. Experimental evaluation on the MixedBag dataset and long-duration sleep recordings demonstrates that RS4 achieves a 78% reduction in computation time while maintaining 92% of the pattern coverage compared to exhaustive methods. The results highlight the potential of combining structural clustering with distance-based refinement for efficient time series summarization.