||We consider the problem of choosing influential members within a social network, in order to disseminate a message as widely as possible. While this so-called problem of influence maximisation has been widely studied, little work considers partially-observable networks, where only part of a network is visible to the decision maker. Yet, this is critical in many applications, where an organisation needs to distribute its message far beyond its boundaries and beyond its usual sphere of influence. In this paper, we show that existing algorithms are not sufficient to handle such scenarios. To address this, we propose a set of novel adaptive algorithms that perform well in partially observable settings, achieving an up to 18% improvement on the non-adaptive state of the art.