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With machine learning techniques widely used to automate Android malware detection, it is important to investigate the robustness of these methods against evasion attacks. A recent work has proposed a novel problem-space attack on Android malware classifiers, where adversarial examples are generated by transforming Android malware samples while satisfying practical constraints. Aimed to address its limitations, we propose a new attack called EAGLE (Evasion Attacks Guided by Local Explanations), whose key idea is to leverage local explanations to guide the search for adversarial examples. We present a generic algorithmic framework for EAGLE attacks, which can be customized with specific feature increase and decrease operations to evade Android malware classifiers trained on different types of count features. We overcome practical challenges in implementing these operations for four different types of Android malware classifiers. Using two Android malware datasets, our results show that EAGLE attacks can be highly effective at finding functionable adversarial examples. We study the attack transferrability of malware variants created by EAGLE attacks across classifiers built with different classification models or trained on different types of count features. Our research further demonstrates that ensemble classifiers trained from multiple types of count features are not immune to EAGLE attacks. We also discuss possible defense mechanisms against EAGLE attacks.