1. A computer-implemented method for online learning of click-through rates on federated result blocks from a plurality of federated sources, comprising: receiving, by an enterprise search engine at a computer, click-through feedback for the federated result blocks received from known federated sources in response to a query; determining a click probability for federated block results received from unknown federated sources based on a plurality of unknown federated block features, the unknown federated sources comprising federated sources which are previously unknown to the enterprise search engine, the plurality of unknown federated block features comprising a block type ID and a query ID associated with an unknown federated source, wherein determining the click probability comprises executing a learning algorithm on a query log comprising a mapping of the block type ID, the query ID, and a performance score of the block type ID for the query ID, wherein the algorithm is based on a probit regression model that maps discrete or real-valued input features to probabilities; predicting the click-through rate of the federated block results received from the unknown federated sources from the determined click probability; assigning a weight of each of the federated result blocks, wherein assigning the weight comprises assigning the weight to each of the federated result blocks received from known federated sources based on the received click-through feedback and assigning the weight for the federated result blocks received from the unknown federated sources based on the predicted click-through rate; ranking, by the enterprise search engine, the federated result blocks based on the assigned weights; and generating, by the enterprise search engine, a search results page for displaying the ranked federated result blocks, the ranked federated result blocks being displayed in one of a plurality of hierarchical slots based on a predetermined threshold value.