The ability of contrastive language-image pretraining (CLIP) to pair text and image embeddings has notably enhanced the performance of video retrieval, enabling systems to efficiently utilize free-text searches within a CLIP-based embeddings index. Despite these technological improvements, disparities in user performance have been observed in recent video retrieval competitions, such as the Video Browser Showdown 2024 (VBS2024). We conclude that not only a highly accurate search index is important, but also factors such as runtime performance, system usability, browsing context, and interaction features. Addressing these issues, we introduce an enhanced version of the diveXplore system, merging the features of diveXplore 2024 and diveXB, our previously used systems for VBS2024 and IVR4B 2023. The system is designed for simple usage by video retrieval beginners, and optimized for fast and accurate search with clear content structuring and browsing context. The current diveXplore system uses an index of CLIP embeddings as the basic search method, including temporal queries, but also allows other search types (e.g., OCR, speech, metadata, etc.). It integrates a new video preview component with enriched browsing context, including a video summary of the related video, a video player, and a list of similar videos.