Phishing websites are everywhere, and countermeasures based on static blocklists cannot cope with such a threat. To address this problem, state-of-the-art solutions entail the application of machine learning (ML) to detect phishing websites by checking if they visually resemble webpages of well-known brands. These techniques have achieved promising results in research and, consequently, some security companies began to deploy them also in their phishing detection systems (PDS). However, ML methods are not perfect and some samples are bound to bypass even production-grade PDS. In this paper, we scrutinize whether 'genuine phishing websites' that evade 'commercial ML-based PDS' represent a problem "in reality". Although nobody likes landing on a phishing webpage, a false negative may not lead to serious consequences if the users (i.e., the actual target of phishing) can recognize that "something is phishy". Practically, we carry out the first user-study (N=126) wherein we assess whether unsuspecting users (having diverse backgrounds) are deceived by 'adversarial' phishing webpages that evaded a real PDS. We found that some well-crafted adversarial webpages can trick most participants (even IT experts), albeit others are easily recognized by most users. Our study is relevant for practitioners, since it allows prioritizing phishing webpages that simultaneously fool (i) machines and (ii) humans -- i.e., their intended targets.