We present the idea and implementation details of a highly effective and reliable e-mail filtering technique. At the core of the component-based architecture is a novel combination of an enhanced self-learning variant of greylisting with a reputation-based trust mechanism. These strategies provide separate feature extraction and classification components with the opportunity of utilizing the time between two delivery attempts of an e-mail message. The approach presented features a very high spam blocking rate and also minimizes the workload on the client side, as no responsibility for messages classified as spam is taken. The reputation-based trust mechanism decreases the delay in the transfer process of e-mail messages from reliable senders and also reduces the number of erroneously blocked legitimate messages.