Current Portuguese Automated Fact-Checking (AFC) research often relies on datasets lacking integrated external evidence crucial for comprehensive verification. This study addresses this gap by systematically enriching Portuguese misinformation datasets. We retrieve web evidence by simulating user information-seeking behavior, guided by core claims extracted using Large Language Models (LLMs). Additionally, we apply a semi-automated validation framework to enhance dataset reliability.Our analysis reveals that inherent dataset characteristics impact data properties, evidence retrieval, and AFC model performance. While enrichment generally improves detection, its efficacy varies, influenced by challenges such as self-reinforcing online misinformation and API limitations. This work contributes enriched datasets, associating original texts with retrieved evidence and LLM-extracted claims, to foster future evidence-based fact-checking research.The code and enriched data for this study is available at https://github.com/ju-resplande/pt_afc.