In this paper, we present Memento 2.0, an improved version of our system which first participated in the Lifelog Search Challenge 2021. Memento 2.0 employs image-text embeddings derived from two CLIP models (ViT-L/14 and ResNet-50x64) and adopts a weighted ensemble approach to derive a combined final ranking. Our approach significantly improves the performance over the baseline LSC’21 system. We additionally make important updates to the system’s user interface after analysing the shortcomings to make it more efficient and better suited to the needs of the Lifelog Search Challenge.