In today's digital age, misinformation spreads like wildfire across the internet, often blurring the lines between fact and fiction. The rise of "fake news" poses a significant threat, impacting everything from public discourse to political elections. But what if artificial intelligence could help us sift through the noise and identify misleading content? New research explores how Large Vision-Language Models (LVLMs) can detect multimodal misinformation—false information conveyed through a combination of text and images. Traditional methods often struggle with this complex landscape, either focusing solely on text or assuming supporting evidence is readily available. This research introduces a novel approach that not only analyzes both text and images simultaneously but also actively retrieves evidence from a vast corpus of data to verify claims. This two-pronged method involves a re-ranking system called LVLM4EV, which prioritizes the most relevant evidence by combining the strengths of LLMs and LVLMs. Subsequently, LVLM4FV, a fact verification component, steps in to judge the veracity of the claim based on the gathered evidence. One of the key hurdles researchers face is the lack of fully labeled datasets for this complex task. The study addresses this by meticulously annotating a more complete subset of data, allowing for a fairer evaluation of performance. The results are encouraging, demonstrating that the combined LLM/LVLM strategy outperforms existing methods, particularly in identifying contextually relevant images. This approach highlights the potential of AI to not just understand what's being said, but also to analyze the visual context and verify claims against existing data – a critical step towards combating misinformation online. However, challenges remain. The researchers point out the need for larger, more thoroughly annotated datasets and improvements in the model’s ability to handle lengthy textual evidence. Future work will also focus on explaining the model's reasoning, making its decision-making process more transparent and trustworthy.