Progress in gaze-informed multimedia retrieval has been constrained by the absence of datasets that capture visual attention in realistic, goal-driven search scenarios. To address this, we present a new eye-tracking dataset collected from 24 participants performing five goal-driven tasks using two multimedia collections: the Vimeo Creative Commons Collection (V3C) and ImageNet. Tasks include ad-hoc exploration, known-item exploration, textual ranking, visual ranking, and interactive retrieval. In total, the dataset comprises over 6 h of gaze recordings, amounting to approximately 1.4 million gaze points, along with synchronized interaction logs and detailed task metadata. This multimodal resource enables fine-grained analysis of visual attention, user strategies, and search behavior across diverse task types and multimedia domains. Potential applications include gaze-informed content ranking, task-aware saliency prediction, media quality assessment, and cross-dataset benchmarking. By making this dataset available at https://osf.io/rzp64 , we aim to provide a reproducible, versatile resource that advances the study of user-adaptive multimedia retrieval.