How does visual working memory (WM) store the binding between different features of a visual object (like colour, orientation, and location), and does memorizing these bindings require additional resources beyond memorizing individual features? These questions have traditionally been addressed by comparing performance across different types of change detection task. More recently, experimental tasks such as analogue (cued) recall, combined with analysis methods including Bayesian hypothesis testing and formal model comparison, have shed new light on the properties of WM. A significant new perspective is that noise in neural representation limits the precision of recall, and several recent models incorporate this view to account for failures of binding in WM. We review the literature on feature binding with a focus on these new developments and discuss their implications for the interpretation of classical findings.